
import warnings
warnings.filterwarnings('ignore')


# data and visualization
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px


# pre-processing
from sklearn.impute import KNNImputer
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import MinMaxScaler
from sklearn_pandas import DataFrameMapper #Normalization


# prediction

# model building
from sklearn.model_selection import GridSearchCV

# model evaluation
from sklearn import metrics
from sklearn.metrics import accuracy_score, mean_squared_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import r2_score
from sklearn.metrics import plot_confusion_matrix

# random forest classifier
from sklearn.ensemble import RandomForestClassifier
# gradient boosting
from sklearn.ensemble import GradientBoostingClassifier
# KNN
from sklearn.neighbors import KNeighborsClassifier
# adaboost
from sklearn.ensemble import AdaBoostRegressor
# gradient boosting
from sklearn.ensemble import GradientBoostingClassifier, GradientBoostingRegressor
#SVR
from sklearn.svm import SVR
# random forest regressor
from sklearn.ensemble import RandomForestRegressor
#Decision Tree Regressor
from sklearn.tree import DecisionTreeRegressor
# SVM
from sklearn import svm
# PCA
from sklearn.decomposition import PCA
#isomap
from sklearn.manifold import Isomap

from sklearn.linear_model import Lasso
from sklearn.linear_model import Ridge
from yellowbrick.regressor import residuals_plot

from sklearn import linear_model

from yellowbrick.regressor import PredictionError

from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score

from sklearn.svm import SVC  
from sklearn.metrics import classification_report, confusion_matrix  

# set seed for reproducible results
RSEED = 10


# import us_landarea data
us_landarea = pd.read_csv('data/CountyData/5296US_landarea.csv')
us_landarea.sample(5, random_state=RSEED)


us_landarea.shape

(3111, 4)


us_landarea.dtypes

FIPS           int64
State         object
County        object
Land Area    float64
dtype: object


us_landarea.isnull().sum()

FIPS         0
State        0
County       0
Land Area    0
dtype: int64


# convert the type of fips to string and fill the missing zeros at the beginning
us_landarea['FIPS'] = us_landarea['FIPS'].astype(str)
us_landarea['FIPS'] = us_landarea['FIPS'].str.zfill(5)


us_landarea.nlargest(10, 'Land Area')


s = us_landarea.groupby(['State'])['County'].count().reset_index(name='Num Of Counties')


s_top5 = s.nlargest(5, 'Num Of Counties')


plt.figure(figsize=(8, 6))
plt.bar(s_top5['State'], s_top5['Num Of Counties'], color = '#b3cde3', edgecolor='black')
# plt.xticks([1, 2, 3, 4])
plt.title('Number of Counties by State', fontsize=14)
plt.xlabel('State', fontsize=12)
plt.ylabel('Number of Counties', fontsize=12)
sns.despine(top=True, right=True, bottom=False, left=True)
plt.tick_params(axis='both', which='both', bottom=False, top=False, left=False)
plt.show()


# import county_complete data
county_complete = pd.read_csv('data/GeneralDemographicData/county_complete.csv')
county_complete.sample(5, random_state=RSEED)


county_complete.shape

(3142, 188)


county_complete.dtypes

fips                         int64
state                       object
name                        object
pop2000                    float64
pop2010                      int64
                            ...   
uninsured_under_19_2019    float64
uninsured_under_6_2019     float64
veterans_2019              float64
white_2019                 float64
white_not_hispanic_2019    float64
Length: 188, dtype: object


# convert fips column to string and add the missing 0 at the begining
county_complete['fips'] = county_complete['fips'].astype(str)
county_complete['fips'] = county_complete['fips'].str.zfill(5)

from urllib.request import urlopen
import json
with urlopen('https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json') as response:
    counties = json.load(response)


import plotly.express as px

fig = px.choropleth(county_complete, geojson=counties, locations='fips', color='unemployment_rate_2017',
                           color_continuous_scale="Viridis",
                           range_color=(0, 12),
                           scope="usa",
                           labels={'unemployment_rate_2017':'Unemployment Rate', 'name':'County'},
                           hover_data=["name"]
                          )
fig.update_layout(
    title_text = 'Unemployment Rate By Counties 2017<br>(Hover for breakdown)',
)
fig.show()


fig = px.choropleth(county_complete, geojson=counties, locations='fips', color='unemployment_rate_2019',
                           color_continuous_scale="Viridis",
                           range_color=(0, 12),
                           scope="usa",
                           labels={'unemployment_rate_2019':'Unemployment Rate', 'name':'County'},
                           hover_data=["name"]
                          )
fig.update_layout(
    title_text = 'Unemployment rate among those ages 20-64 (2015-2019).<br>(Hover for breakdown)',
)
fig.show()


merged_data1 = county_complete.copy().merge(right = us_landarea.copy(), left_on = ['fips'], right_on = ['FIPS'])
merged_data1


#remove duplicate columns after the merge
merged_data1 = merged_data1.drop(['FIPS', 'State', 'County'], 1)
merged_data1


# import life expectancy data
life_expectancy = pd.read_excel('data/LifeExpectancyData/IHME_USA_LIFE_EXPECTANCY_1987_2007_Y2011M06D16.XLSX')
life_expectancy.head()


life_expectancy.shape

(66087, 24)


life_expectancy.dtypes

fips                                                              int64
State                                                            object
County                                                           object
Year                                                              int64
Male life expectancy (years)                                    float64
Years behind international frontier (male)                       object
Female life expectancy (years)                                  float64
Years behind international frontier (female)                     object
White male life expectancy (years)                              float64
White female life expectancy (years)                            float64
Black male life expectancy (years)                              float64
Black female life expectancy (years)                            float64
Closest ranked countries for male life expectancy (higher)       object
Closest ranked countries for female life expectancy (higher)     object
Closest ranked countries for male life expectancy (lower)        object
Closest ranked countries for female life expectancy (lower)      object
Rank (male)                                                       int64
Rank (female)                                                     int64
Male life expectancy change 1987 to 2007 (years)                float64
Female life expectancy change 1987 to 2007 (years)              float64
Male life expectancy change 1987 to 1997 (years)                float64
Female life expectancy change 1987 to 1997 (years)              float64
Male life expectancy change 1997 to 2007 (years)                float64
Female life expectancy change 1997 to 2007 (years)              float64
dtype: object


life_expectancy_null = life_expectancy.isnull().sum().reset_index(name='Null Count')
life_expectancy_null.head()


life_expectancy_null = life_expectancy_null[life_expectancy_null['Null Count'] != 0]
life_expectancy_null


state_list = ['CALIFORNIA','FLORIDA','SOUTH DAKOTA','WYOMING']

filtered_df = life_expectancy[life_expectancy['State'].isin(state_list)]


ax = filtered_df.pivot_table(values="Rank (male)",index="Year",columns="State").plot()
ax.set_title("Rank(male) over the years")
ax.set_xlabel("Year")
ax.set_ylabel("Rank(male)")
ax.locator_params(integer=True)


# convert fips column to string and add the missing 0 at the begining
life_expectancy['fips'] = life_expectancy['fips'].astype(str)
life_expectancy['fips'] = life_expectancy['fips'].str.zfill(5)


merged_data2 = merged_data1.copy().merge(right = life_expectancy.copy(), left_on=['fips'], right_on=['fips'])
merged_data2


#remove duplicate columns after the merge
merged_data2 = merged_data2.drop(['State', 'County'], 1)
merged_data2


# import 1976-2020 house data
house_elections = pd.read_csv('data/USElectionResults19762020/1976-2020-house.csv')
house_elections.sample(5, random_state=RSEED)


house_elections.shape

(31103, 20)


house_elections.dtypes

year               int64
state             object
state_po          object
state_fips         int64
state_cen          int64
state_ic           int64
office            object
district           int64
stage             object
runoff            object
special             bool
candidate         object
party             object
writein             bool
mode              object
candidatevotes     int64
totalvotes         int64
unofficial          bool
version            int64
fusion_ticket       bool
dtype: object


house_elections = house_elections.drop(['state_po', 'state_fips', 'state_cen', 'state_ic', 'stage', 'runoff', 'special', 'writein', 'mode', 'unofficial', 'version', 'fusion_ticket'], 1)
house_elections


house_elections.isnull().sum()

year                 0
state                0
office               0
district             0
candidate            0
party             3620
candidatevotes       0
totalvotes           0
dtype: int64


viz_df = house_elections[house_elections['year'] == 2020]
viz_df


state_list = ['CALIFORNIA','FLORIDA','SOUTH DAKOTA','WYOMING']
viz_df = viz_df[viz_df['state'].isin(state_list)]
viz_df


# get the top 5 candidates with most votes
candidate_top5 = viz_df.groupby('candidate', as_index=False)['candidatevotes'].sum()
candidate_top5 = candidate_top5.nlargest(5, 'candidatevotes')
candidate_top5


plt.figure(figsize=(12, 6))
plt.bar(candidate_top5['candidate'], candidate_top5['candidatevotes'], color = '#b3cde3', edgecolor='black')
plt.title('Number of Votes by State - 2020', fontsize=14)
plt.xlabel('Candidate', fontsize=12)
plt.ylabel('Number of Votes', fontsize=12)
sns.despine(top=True, right=True, bottom=False, left=True)
plt.tick_params(axis='both', which='both', bottom=False, top=False, left=False)
plt.show()


merged_data2.info(verbose=True)

<class 'pandas.core.frame.DataFrame'>
Int64Index: 65205 entries, 0 to 65204
Data columns (total 210 columns):
 #    Column                                                        Dtype  
---   ------                                                        -----  
 0    fips                                                          object 
 1    state                                                         object 
 2    name                                                          object 
 3    pop2000                                                       float64
 4    pop2010                                                       int64  
 5    pop2011                                                       float64
 6    pop2012                                                       float64
 7    pop2013                                                       float64
 8    pop2014                                                       float64
 9    pop2015                                                       float64
 10   pop2016                                                       float64
 11   pop2017                                                       float64
 12   age_under_5_2010                                              float64
 13   age_under_5_2017                                              float64
 14   age_under_18_2010                                             float64
 15   age_over_65_2010                                              float64
 16   age_over_65_2017                                              float64
 17   median_age_2017                                               float64
 18   female_2010                                                   float64
 19   white_2010                                                    float64
 20   black_2010                                                    float64
 21   black_2017                                                    float64
 22   native_2010                                                   float64
 23   native_2017                                                   float64
 24   asian_2010                                                    float64
 25   asian_2017                                                    float64
 26   pac_isl_2010                                                  float64
 27   pac_isl_2017                                                  float64
 28   other_single_race_2017                                        float64
 29   two_plus_races_2010                                           float64
 30   two_plus_races_2017                                           float64
 31   hispanic_2010                                                 float64
 32   hispanic_2017                                                 float64
 33   white_not_hispanic_2010                                       float64
 34   white_not_hispanic_2017                                       float64
 35   speak_english_only_2017                                       float64
 36   no_move_in_one_plus_year_2010                                 float64
 37   foreign_born_2010                                             float64
 38   foreign_spoken_at_home_2010                                   float64
 39   women_16_to_50_birth_rate_2017                                float64
 40   hs_grad_2010                                                  float64
 41   hs_grad_2016                                                  float64
 42   hs_grad_2017                                                  float64
 43   some_college_2016                                             float64
 44   some_college_2017                                             float64
 45   bachelors_2010                                                float64
 46   bachelors_2016                                                float64
 47   bachelors_2017                                                float64
 48   veterans_2010                                                 int64  
 49   veterans_2017                                                 float64
 50   mean_work_travel_2010                                         float64
 51   mean_work_travel_2017                                         float64
 52   broadband_2017                                                float64
 53   computer_2017                                                 float64
 54   housing_units_2010                                            int64  
 55   homeownership_2010                                            float64
 56   housing_multi_unit_2010                                       float64
 57   median_val_owner_occupied_2010                                int64  
 58   households_2010                                               int64  
 59   households_2017                                               float64
 60   persons_per_household_2010                                    float64
 61   persons_per_household_2017                                    float64
 62   per_capita_income_2010                                        int64  
 63   per_capita_income_2017                                        float64
 64   metro_2013                                                    float64
 65   median_household_income_2010                                  int64  
 66   median_household_income_2016                                  float64
 67   median_household_income_2017                                  float64
 68   private_nonfarm_establishments_2009                           int64  
 69   private_nonfarm_employment_2009                               int64  
 70   percent_change_private_nonfarm_employment_2009                float64
 71   nonemployment_establishments_2009                             float64
 72   firms_2007                                                    float64
 73   black_owned_firms_2007                                        float64
 74   native_owned_firms_2007                                       float64
 75   asian_owned_firms_2007                                        float64
 76   pac_isl_owned_firms_2007                                      float64
 77   hispanic_owned_firms_2007                                     float64
 78   women_owned_firms_2007                                        float64
 79   manufacturer_shipments_2007                                   float64
 80   mercent_whole_sales_2007                                      float64
 81   sales_2007                                                    float64
 82   sales_per_capita_2007                                         float64
 83   accommodation_food_service_2007                               float64
 84   building_permits_2010                                         int64  
 85   fed_spending_2009                                             float64
 86   area_2010                                                     float64
 87   density_2010                                                  float64
 88   smoking_ban_2010                                              object 
 89   poverty_2010                                                  float64
 90   poverty_2016                                                  float64
 91   poverty_2017                                                  float64
 92   poverty_age_under_5_2017                                      float64
 93   poverty_age_under_18_2017                                     float64
 94   civilian_labor_force_2007                                     float64
 95   employed_2007                                                 float64
 96   unemployed_2007                                               float64
 97   unemployment_rate_2007                                        float64
 98   civilian_labor_force_2008                                     float64
 99   employed_2008                                                 float64
 100  unemployed_2008                                               float64
 101  unemployment_rate_2008                                        float64
 102  civilian_labor_force_2009                                     float64
 103  employed_2009                                                 float64
 104  unemployed_2009                                               float64
 105  unemployment_rate_2009                                        float64
 106  civilian_labor_force_2010                                     float64
 107  employed_2010                                                 float64
 108  unemployed_2010                                               float64
 109  unemployment_rate_2010                                        float64
 110  civilian_labor_force_2011                                     float64
 111  employed_2011                                                 float64
 112  unemployed_2011                                               float64
 113  unemployment_rate_2011                                        float64
 114  civilian_labor_force_2012                                     float64
 115  employed_2012                                                 float64
 116  unemployed_2012                                               float64
 117  unemployment_rate_2012                                        float64
 118  civilian_labor_force_2013                                     float64
 119  employed_2013                                                 float64
 120  unemployed_2013                                               float64
 121  unemployment_rate_2013                                        float64
 122  civilian_labor_force_2014                                     float64
 123  employed_2014                                                 float64
 124  unemployed_2014                                               float64
 125  unemployment_rate_2014                                        float64
 126  civilian_labor_force_2015                                     float64
 127  employed_2015                                                 float64
 128  unemployed_2015                                               float64
 129  unemployment_rate_2015                                        float64
 130  civilian_labor_force_2016                                     float64
 131  employed_2016                                                 float64
 132  unemployed_2016                                               float64
 133  unemployment_rate_2016                                        float64
 134  uninsured_2017                                                float64
 135  uninsured_age_under_6_2017                                    object 
 136  uninsured_age_under_19_2017                                   float64
 137  uninsured_age_over_74_2017                                    float64
 138  civilian_labor_force_2017                                     float64
 139  employed_2017                                                 float64
 140  unemployed_2017                                               float64
 141  unemployment_rate_2017                                        float64
 142  age_over_18_2019                                              float64
 143  age_over_65_2019                                              float64
 144  age_over_85_2019                                              float64
 145  age_under_5_2019                                              float64
 146  asian_2019                                                    float64
 147  avg_family_size_2019                                          float64
 148  bachelors_2019                                                float64
 149  black_2019                                                    float64
 150  hispanic_2019                                                 float64
 151  household_has_broadband_2019                                  float64
 152  household_has_computer_2019                                   float64
 153  household_has_smartphone_2019                                 float64
 154  households_2019                                               int64  
 155  households_speak_asian_or_pac_isl_2019                        float64
 156  households_speak_limited_english_2019                         float64
 157  households_speak_other_2019                                   float64
 158  households_speak_other_indo_euro_lang_2019                    float64
 159  households_speak_spanish_2019                                 float64
 160  housing_mobile_homes_2019                                     float64
 161  housing_one_unit_structures_2019                              float64
 162  housing_two_unit_structures_2019                              float64
 163  hs_grad_2019                                                  float64
 164  mean_household_income_2019                                    int64  
 165  mean_work_travel_2019                                         float64
 166  median_age_2019                                               float64
 167  median_household_income_2019                                  int64  
 168  median_individual_income_2019                                 int64  
 169  median_individual_income_age_25plus_2019                      int64  
 170  native_2019                                                   float64
 171  other_single_race_2019                                        float64
 172  pac_isl_2019                                                  float64
 173  per_capita_income_2019                                        int64  
 174  persons_per_household_2019                                    float64
 175  pop_2019                                                      int64  
 176  poverty_2019                                                  float64
 177  poverty_65_and_over_2019                                      float64
 178  poverty_under_18_2019                                         float64
 179  two_plus_races_2019                                           float64
 180  unemployment_rate_2019                                        float64
 181  uninsured_2019                                                float64
 182  uninsured_65_and_older_2019                                   float64
 183  uninsured_under_19_2019                                       float64
 184  uninsured_under_6_2019                                        float64
 185  veterans_2019                                                 float64
 186  white_2019                                                    float64
 187  white_not_hispanic_2019                                       float64
 188  Land Area                                                     float64
 189  Year                                                          int64  
 190  Male life expectancy (years)                                  float64
 191  Years behind international frontier (male)                    object 
 192  Female life expectancy (years)                                float64
 193  Years behind international frontier (female)                  object 
 194  White male life expectancy (years)                            float64
 195  White female life expectancy (years)                          float64
 196  Black male life expectancy (years)                            float64
 197  Black female life expectancy (years)                          float64
 198  Closest ranked countries for male life expectancy (higher)    object 
 199  Closest ranked countries for female life expectancy (higher)  object 
 200  Closest ranked countries for male life expectancy (lower)     object 
 201  Closest ranked countries for female life expectancy (lower)   object 
 202  Rank (male)                                                   int64  
 203  Rank (female)                                                 int64  
 204  Male life expectancy change 1987 to 2007 (years)              float64
 205  Female life expectancy change 1987 to 2007 (years)            float64
 206  Male life expectancy change 1987 to 1997 (years)              float64
 207  Female life expectancy change 1987 to 1997 (years)            float64
 208  Male life expectancy change 1997 to 2007 (years)              float64
 209  Female life expectancy change 1997 to 2007 (years)            float64
dtypes: float64(179), int64(20), object(11)
memory usage: 105.0+ MB


merged_data2['uninsured_age_under_6_2017'].unique()

array(['1.1', '2.4', '4.1', '2.7', '3.9', '0', '1.5', '0.3', '6', '1.8',
       '3.7', '1.7', '12.5', '4.3', '4.6', '2', '6.5', '5', '0.2', '4.5',
       '2.9', '1.4', '1.9', '2.2', '8.3', '0.1', '7.9', '5.9', '2.3',
       '0.9', '2.1', '1', '4', '14.4', '3.6', '2.5', '3', '5.2', '1.3',
       '0.5', '3.5', '0.7', '0.6', '1.6', '3.4', '2.8', '9.7', '18.5',
       '3.3', '9.2', '9.5', '5.4', '10.3', '5.1', '7.2', '8.9', '8.4',
       '7.3', '10.4', '9.1', '6.9', '6.4', '7.8', '7.5', '0.4', '18', '7',
       '5.5', '3.1', '3.8', '4.8', '5.7', '6.6', '4.4', '8.5', '3.2',
       '2.6', '5.6', '15.7', '4.2', '4.9', '4.7', '16', '5.3', '10.2',
       '15.2', '16.1', '9.8', '9.6', '11.3', '11.5', '11.2', '5.8', '9',
       '13.9', '11.1', '6.1', '1.2', '13.8', '6.8', '15.1', '6.7', '9.9',
       '7.7', '8.2', '10.1', '14.5', '7.4', '7.1', '8.1', '8.8', '9.3',
       '10.8', '16.6', '18.4', '18.2', '10', '17.3', '8.6', '26.6',
       '18.8', '12.1', '11.8', '13', '13.5', '16.9', '12.6', '21.5',
       '14.2', '10.5', '12.9', '16.7', '8', '13.2', '12.2', '36.4',
       '15.4', '10.9', '9.4', '8.7', '17.6', '12', '14.6', '17.1', '19.4',
       '13.3', '0.8', '6.2', '6.3', '17.8', '35.3', '16.4', '36.8', '7.6',
       '22.1', '11.4', '56.8', '13.4', '15', '20.7', '18.7', '17.4',
       '12.3', '11.6', '56.2', '15.8', '21.1', '20.8', '22.7', '17.9',
       '13.6', '11.9', '13.7', '15.9', '12.7', '16.2', '18.1', '18.9',
       '16.3', '11', '20.6', '25.7', '35.6', '29.9', '22.3', '21.4',
       '32.1', '25.8', '24.7', '14.7', '10.6', '10.7', '19.6', '16.8',
       '15.3', '33.8', '25.3', '17', '19.8', '42.9', '36', '20', '17.5',
       '21.6', '18.6', '33.9', '22.8', '30.6', '29.3', '20.2', '14.3',
       '23.4', '14.8', '41.2', '23.6', '42.3', '19.1', '26', '22.9',
       '55.6', '14.1', '24.1', '12.4', '12.8', '19.5', '19.7', '23.3',
       '14.9', '25.2', '19.2', '23.5', '17.2', '21.2', '24.3', '22',
       '11.7', '19.3', '24.8', '26.4', '15.6', '14', '25.4', '51', '25',
       '48.5', '32.4', '25.5', '-', '21.8', '28.6', '18.3', '42.2',
       '21.3', '13.1', '25.9', '24.4', '23', '39.9', '26.7', '28.1'],
      dtype=object)


# replace "-" values with null 
def change(x):
    if x == "-":
        return None   
    else:
        return float(x)
    
merged_data2['uninsured_age_under_6_2017'] = merged_data2['uninsured_age_under_6_2017'].map(change)
merged_data2['uninsured_age_under_6_2017'].unique()

array([ 1.1,  2.4,  4.1,  2.7,  3.9,  0. ,  1.5,  0.3,  6. ,  1.8,  3.7,
        1.7, 12.5,  4.3,  4.6,  2. ,  6.5,  5. ,  0.2,  4.5,  2.9,  1.4,
        1.9,  2.2,  8.3,  0.1,  7.9,  5.9,  2.3,  0.9,  2.1,  1. ,  4. ,
       14.4,  3.6,  2.5,  3. ,  5.2,  1.3,  0.5,  3.5,  0.7,  0.6,  1.6,
        3.4,  2.8,  9.7, 18.5,  3.3,  9.2,  9.5,  5.4, 10.3,  5.1,  7.2,
        8.9,  8.4,  7.3, 10.4,  9.1,  6.9,  6.4,  7.8,  7.5,  0.4, 18. ,
        7. ,  5.5,  3.1,  3.8,  4.8,  5.7,  6.6,  4.4,  8.5,  3.2,  2.6,
        5.6, 15.7,  4.2,  4.9,  4.7, 16. ,  5.3, 10.2, 15.2, 16.1,  9.8,
        9.6, 11.3, 11.5, 11.2,  5.8,  9. , 13.9, 11.1,  6.1,  1.2, 13.8,
        6.8, 15.1,  6.7,  9.9,  7.7,  8.2, 10.1, 14.5,  7.4,  7.1,  8.1,
        8.8,  9.3, 10.8, 16.6, 18.4, 18.2, 10. , 17.3,  8.6, 26.6, 18.8,
       12.1, 11.8, 13. , 13.5, 16.9, 12.6, 21.5, 14.2, 10.5, 12.9, 16.7,
        8. , 13.2, 12.2, 36.4, 15.4, 10.9,  9.4,  8.7, 17.6, 12. , 14.6,
       17.1, 19.4, 13.3,  0.8,  6.2,  6.3, 17.8, 35.3, 16.4, 36.8,  7.6,
       22.1, 11.4, 56.8, 13.4, 15. , 20.7, 18.7, 17.4, 12.3, 11.6, 56.2,
       15.8, 21.1, 20.8, 22.7, 17.9, 13.6, 11.9, 13.7, 15.9, 12.7, 16.2,
       18.1, 18.9, 16.3, 11. , 20.6, 25.7, 35.6, 29.9, 22.3, 21.4, 32.1,
       25.8, 24.7, 14.7, 10.6, 10.7, 19.6, 16.8, 15.3, 33.8, 25.3, 17. ,
       19.8, 42.9, 36. , 20. , 17.5, 21.6, 18.6, 33.9, 22.8, 30.6, 29.3,
       20.2, 14.3, 23.4, 14.8, 41.2, 23.6, 42.3, 19.1, 26. , 22.9, 55.6,
       14.1, 24.1, 12.4, 12.8, 19.5, 19.7, 23.3, 14.9, 25.2, 19.2, 23.5,
       17.2, 21.2, 24.3, 22. , 11.7, 19.3, 24.8, 26.4, 15.6, 14. , 25.4,
       51. , 25. , 48.5, 32.4, 25.5,  nan, 21.8, 28.6, 18.3, 42.2, 21.3,
       13.1, 25.9, 24.4, 23. , 39.9, 26.7, 28.1])


merged_data2['Years behind international frontier (male)'].unique()

array([32, 25, 27, 24, 23, 28, 26, 12, 11, 14, 16, 15, 13, 18, 19, '50+',
       44, 43, 37, 36, 33, 35, 34, 38, 39, 40, 41, 45, 46, 17, 20, 21, 22,
       47, 50, 31, 29, 30, 42, 49, 48, 10, 9, 8, 7, 5, -4, -6, -5, -1, 3,
       4, 2, 6, 1, 0, -2, -3, -7, -8, -11, -13, -9, -10, -12, -14, -16,
       -15], dtype=object)


# replace 50+ values with 50 and values which are less than 0 by 0
def change(x):
    if x == '50+':
        return 50.0
    elif x < 0:
        return 0.0
    else:
        return float(x)


merged_data2['Years behind international frontier (male)'] = merged_data2['Years behind international frontier (male)'].map(change)
merged_data2['Years behind international frontier (male)'].unique()

array([32., 25., 27., 24., 23., 28., 26., 12., 11., 14., 16., 15., 13.,
       18., 19., 50., 44., 43., 37., 36., 33., 35., 34., 38., 39., 40.,
       41., 45., 46., 17., 20., 21., 22., 47., 31., 29., 30., 42., 49.,
       48., 10.,  9.,  8.,  7.,  5.,  0.,  3.,  4.,  2.,  6.,  1.])


merged_data2['Years behind international frontier (female)'].unique()

array([12, 14, 15, 16, 17, 19, 21, 22, 24, 26, 28, 30, 7, 8, 10, 11, 18,
       20, 23, 25, 27, 31, 33, 34, 35, 9, 13, 29, 32, 36, 37, 38, 39, 41,
       43, 6, 40, 42, 44, 45, 3, 4, -4, -5, -3, -2, 1, 5, 2, 0, -1, -6,
       -12, -16, 46, 48, 49, 50, '50+', 47], dtype=object)


merged_data2['Years behind international frontier (female)'] = merged_data2['Years behind international frontier (female)'].map(change)
merged_data2['Years behind international frontier (female)'].unique()

array([12., 14., 15., 16., 17., 19., 21., 22., 24., 26., 28., 30.,  7.,
        8., 10., 11., 18., 20., 23., 25., 27., 31., 33., 34., 35.,  9.,
       13., 29., 32., 36., 37., 38., 39., 41., 43.,  6., 40., 42., 44.,
       45.,  3.,  4.,  0.,  1.,  5.,  2., 46., 48., 49., 50., 47.])


merged_data2 = merged_data2.drop('fips', 1)


# before dropping null values
house_elections.isnull().sum()

year                 0
state                0
office               0
district             0
candidate            0
party             3620
candidatevotes       0
totalvotes           0
dtype: int64


house_elections = house_elections.dropna()


# after dropping null values
house_elections.isnull().sum()

year              0
state             0
office            0
district          0
candidate         0
party             0
candidatevotes    0
totalvotes        0
dtype: int64


columns_with_null = merged_data2.columns[merged_data2.isna().any()].tolist()
columns_with_null

['black_2010',
 'native_2010',
 'asian_2010',
 'pac_isl_2010',
 'percent_change_private_nonfarm_employment_2009',
 'nonemployment_establishments_2009',
 'firms_2007',
 'black_owned_firms_2007',
 'native_owned_firms_2007',
 'asian_owned_firms_2007',
 'pac_isl_owned_firms_2007',
 'hispanic_owned_firms_2007',
 'women_owned_firms_2007',
 'manufacturer_shipments_2007',
 'mercent_whole_sales_2007',
 'sales_2007',
 'sales_per_capita_2007',
 'accommodation_food_service_2007',
 'fed_spending_2009',
 'smoking_ban_2010',
 'poverty_age_under_5_2017',
 'uninsured_age_under_6_2017',
 'mean_work_travel_2019',
 'poverty_2019',
 'poverty_65_and_over_2019',
 'poverty_under_18_2019',
 'Black male life expectancy (years)',
 'Black female life expectancy (years)',
 'Closest ranked countries for male life expectancy (higher)',
 'Closest ranked countries for female life expectancy (higher)']


len(columns_with_null)

30


# create different datasets for numeric and categorical variables
numeric_features= merged_data2.select_dtypes(include='number')
categorical_features = merged_data2.select_dtypes(include='object')


# drop categorical variables
merged_data2.drop(categorical_features.columns, axis=1, inplace=True)


#Normalize the data
mapper = DataFrameMapper([(merged_data2.columns, StandardScaler())])
scaled_features = mapper.fit_transform(merged_data2.copy(), 4)
merged_data2_scaled = pd.DataFrame(scaled_features, index=merged_data2.index, columns=merged_data2.columns)
merged_data2_scaled = merged_data2_scaled.join(categorical_features[['state']])
merged_data2_scaled = merged_data2_scaled.join(categorical_features[['name']])
merged_data2_scaled


# missing values by variable
merged_data2_scaled.isna().sum().sort_values(ascending = False).head(10)

pac_isl_owned_firms_2007                63840
native_owned_firms_2007                 55356
asian_owned_firms_2007                  50043
Black male life expectancy (years)      50015
black_owned_firms_2007                  49266
hispanic_owned_firms_2007               49014
Black female life expectancy (years)    47108
poverty_under_18_2019                   38829
poverty_65_and_over_2019                38829
poverty_2019                            38829
dtype: int64


# use KNN to impute missing values
imputer = KNNImputer(n_neighbors=5, weights="distance")
imputed = imputer.fit_transform(merged_data2_scaled.drop(['state', 'name'], 1))

# replace values in the dataset with new imputed values
merged_data2_scaled[merged_data2_scaled.drop(['state', 'name'], 1).columns] = imputed


# missing values by variable
merged_data2_scaled.isna().sum().sort_values(ascending = False).head(10)

pop2000                        0
age_over_85_2019               0
uninsured_2017                 0
uninsured_age_under_6_2017     0
uninsured_age_under_19_2017    0
uninsured_age_over_74_2017     0
civilian_labor_force_2017      0
employed_2017                  0
unemployed_2017                0
unemployment_rate_2017         0
dtype: int64


# Create correlation matrix
corr_matrix = merged_data2_scaled.drop(['state', 'name'], 1).corr().abs()

# Select upper triangle of correlation matrix
upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool))

# Find features with correlation greater than 0.8
to_drop = [column for column in upper.columns if any(upper[column] > 0.8)]

# Drop features 
merged_data2_scaled.drop(to_drop, axis=1, inplace=True)


# dropped columns
to_drop

['pop2010',
 'pop2011',
 'pop2012',
 'pop2013',
 'pop2014',
 'pop2015',
 'pop2016',
 'pop2017',
 'age_under_5_2017',
 'age_under_18_2010',
 'age_over_65_2017',
 'median_age_2017',
 'black_2010',
 'black_2017',
 'native_2017',
 'asian_2017',
 'hispanic_2017',
 'white_not_hispanic_2010',
 'white_not_hispanic_2017',
 'speak_english_only_2017',
 'foreign_born_2010',
 'foreign_spoken_at_home_2010',
 'hs_grad_2016',
 'hs_grad_2017',
 'some_college_2017',
 'bachelors_2016',
 'bachelors_2017',
 'veterans_2010',
 'mean_work_travel_2017',
 'computer_2017',
 'housing_units_2010',
 'households_2010',
 'households_2017',
 'per_capita_income_2017',
 'median_household_income_2010',
 'median_household_income_2016',
 'median_household_income_2017',
 'private_nonfarm_establishments_2009',
 'private_nonfarm_employment_2009',
 'nonemployment_establishments_2009',
 'firms_2007',
 'black_owned_firms_2007',
 'asian_owned_firms_2007',
 'hispanic_owned_firms_2007',
 'manufacturer_shipments_2007',
 'mercent_whole_sales_2007',
 'sales_2007',
 'accommodation_food_service_2007',
 'fed_spending_2009',
 'poverty_2016',
 'poverty_2017',
 'poverty_age_under_5_2017',
 'poverty_age_under_18_2017',
 'civilian_labor_force_2007',
 'employed_2007',
 'unemployed_2007',
 'civilian_labor_force_2008',
 'employed_2008',
 'unemployed_2008',
 'unemployment_rate_2008',
 'civilian_labor_force_2009',
 'employed_2009',
 'unemployed_2009',
 'unemployment_rate_2009',
 'civilian_labor_force_2010',
 'employed_2010',
 'unemployed_2010',
 'unemployment_rate_2010',
 'civilian_labor_force_2011',
 'employed_2011',
 'unemployed_2011',
 'unemployment_rate_2011',
 'civilian_labor_force_2012',
 'employed_2012',
 'unemployed_2012',
 'unemployment_rate_2012',
 'civilian_labor_force_2013',
 'employed_2013',
 'unemployed_2013',
 'unemployment_rate_2013',
 'civilian_labor_force_2014',
 'employed_2014',
 'unemployed_2014',
 'unemployment_rate_2014',
 'civilian_labor_force_2015',
 'employed_2015',
 'unemployed_2015',
 'unemployment_rate_2015',
 'civilian_labor_force_2016',
 'employed_2016',
 'unemployed_2016',
 'unemployment_rate_2016',
 'uninsured_age_under_19_2017',
 'civilian_labor_force_2017',
 'employed_2017',
 'unemployed_2017',
 'unemployment_rate_2017',
 'age_over_18_2019',
 'age_over_65_2019',
 'age_under_5_2019',
 'asian_2019',
 'avg_family_size_2019',
 'bachelors_2019',
 'black_2019',
 'hispanic_2019',
 'household_has_broadband_2019',
 'household_has_computer_2019',
 'household_has_smartphone_2019',
 'households_2019',
 'households_speak_asian_or_pac_isl_2019',
 'households_speak_limited_english_2019',
 'households_speak_spanish_2019',
 'housing_mobile_homes_2019',
 'housing_two_unit_structures_2019',
 'hs_grad_2019',
 'mean_household_income_2019',
 'mean_work_travel_2019',
 'median_age_2019',
 'median_household_income_2019',
 'median_individual_income_2019',
 'median_individual_income_age_25plus_2019',
 'native_2019',
 'other_single_race_2019',
 'per_capita_income_2019',
 'persons_per_household_2019',
 'pop_2019',
 'poverty_2019',
 'poverty_under_18_2019',
 'two_plus_races_2019',
 'uninsured_2019',
 'uninsured_under_19_2019',
 'veterans_2019',
 'white_2019',
 'white_not_hispanic_2019',
 'Land Area',
 'Years behind international frontier (male)',
 'Female life expectancy (years)',
 'White male life expectancy (years)',
 'White female life expectancy (years)',
 'Black male life expectancy (years)',
 'Black female life expectancy (years)',
 'Rank (male)',
 'Rank (female)',
 'Male life expectancy change 1997 to 2007 (years)',
 'Female life expectancy change 1997 to 2007 (years)']


# updated dataframe
merged_data2_scaled


# df = merged_data2_scaled
state_list = ['California','Florida','South Dakota','Wyoming']
df = merged_data2_scaled[merged_data2_scaled['state'].isin(state_list)]
df = df.groupby(['state', 'name'], as_index=False).mean()

numeric_features= df.select_dtypes(include='number')
X = df[numeric_features.columns]

pca = PCA(n_components=2)
components = pca.fit_transform(X)

fig = px.scatter(components, x=0, y=1, color=df['state'], 
                 labels={
                     "0": "PC1",
                     "1": "PC2",
                 },
                title="County Clusters by States")
fig.show()


print('Explained variation per principal component: {}'.format(pca.explained_variance_ratio_))

Explained variation per principal component: [0.24543326 0.20686539]


pca_values1 = {}
pca_list1 = np.array(abs(pca.components_[0])).tolist()

for i in pca_list1:
    pca_values1[abs(i)] = pca_list1.index(i)


pca_values1

{0.3699630305828455: 0,
 0.031169914000983345: 1,
 0.12528291034665306: 2,
 0.05925868902325941: 3,
 0.11253740125889117: 4,
 0.05347168774854461: 5,
 0.4180689573778869: 6,
 0.15910926569200434: 7,
 0.15335571372464307: 8,
 0.19121421004022837: 9,
 0.19098849857287814: 10,
 0.12523181280275533: 11,
 0.16004150979551263: 12,
 0.06414068193410842: 13,
 0.06969440842161877: 14,
 0.01822275224631341: 15,
 0.028906823070860437: 16,
 0.11822041349364688: 17,
 0.11196390151179335: 18,
 0.08136854253115473: 19,
 0.11079103046518078: 20,
 0.1520619105739115: 21,
 0.1695412661562914: 22,
 0.31299911095801314: 23,
 0.10317102319569753: 24,
 0.14566240445946366: 25,
 0.12387884055094664: 26,
 0.12399055296175113: 27,
 0.0040448273172473525: 28,
 0.03146461787002884: 29,
 0.007965149212082561: 30,
 0.10252820841645592: 31,
 0.04754411906669883: 32,
 0.21989483685899128: 33,
 0.06318176599730302: 34,
 0.07263450975965997: 35,
 0.020386953393289938: 36,
 0.0910795159630159: 37,
 0.02654873062238773: 38,
 0.04038757571879795: 39,
 0.05996222796817054: 40,
 0.08906815475876562: 41,
 0.009464767236301757: 42,
 0.12920932135364452: 43,
 0.11055186403092941: 44,
 0.14522186176662766: 45,
 0.00437439180148958: 46,
 0.042591421662535406: 47,
 0.05757135339828772: 48,
 0.056785953192417506: 49,
 0.0: 50,
 0.05160224691678352: 51,
 0.025635803653047785: 52,
 0.1547968887493836: 53,
 0.13668191362778015: 54,
 0.12207832460827833: 55,
 0.09019617120850286: 56}


sorted_pca_values1 = dict(sorted(pca_values1.items(), key=lambda item: item[0], reverse=True))
sorted_pca_values1

{0.4180689573778869: 6,
 0.3699630305828455: 0,
 0.31299911095801314: 23,
 0.21989483685899128: 33,
 0.19121421004022837: 9,
 0.19098849857287814: 10,
 0.1695412661562914: 22,
 0.16004150979551263: 12,
 0.15910926569200434: 7,
 0.1547968887493836: 53,
 0.15335571372464307: 8,
 0.1520619105739115: 21,
 0.14566240445946366: 25,
 0.14522186176662766: 45,
 0.13668191362778015: 54,
 0.12920932135364452: 43,
 0.12528291034665306: 2,
 0.12523181280275533: 11,
 0.12399055296175113: 27,
 0.12387884055094664: 26,
 0.12207832460827833: 55,
 0.11822041349364688: 17,
 0.11253740125889117: 4,
 0.11196390151179335: 18,
 0.11079103046518078: 20,
 0.11055186403092941: 44,
 0.10317102319569753: 24,
 0.10252820841645592: 31,
 0.0910795159630159: 37,
 0.09019617120850286: 56,
 0.08906815475876562: 41,
 0.08136854253115473: 19,
 0.07263450975965997: 35,
 0.06969440842161877: 14,
 0.06414068193410842: 13,
 0.06318176599730302: 34,
 0.05996222796817054: 40,
 0.05925868902325941: 3,
 0.05757135339828772: 48,
 0.056785953192417506: 49,
 0.05347168774854461: 5,
 0.05160224691678352: 51,
 0.04754411906669883: 32,
 0.042591421662535406: 47,
 0.04038757571879795: 39,
 0.03146461787002884: 29,
 0.031169914000983345: 1,
 0.028906823070860437: 16,
 0.02654873062238773: 38,
 0.025635803653047785: 52,
 0.020386953393289938: 36,
 0.01822275224631341: 15,
 0.009464767236301757: 42,
 0.007965149212082561: 30,
 0.00437439180148958: 46,
 0.0040448273172473525: 28,
 0.0: 50}


pca_values2 = {}
pca_list2 = np.array(abs(pca.components_[1])).tolist()

for j in pca_list2:
    pca_values2[abs(j)] = pca_list2.index(j)


sorted_pca_values2 = dict(sorted(pca_values2.items(), key=lambda item: item[0], reverse=True))
sorted_pca_values2

{0.4162326449330077: 5,
 0.299267580521829: 42,
 0.2616869989692539: 47,
 0.25019403364624826: 24,
 0.24249097076702422: 25,
 0.2297005903687704: 29,
 0.21833498252920336: 1,
 0.20783076305521903: 36,
 0.186900974533724: 4,
 0.18504564538765791: 38,
 0.16997212695034358: 48,
 0.16395520056692517: 40,
 0.1591879163395894: 26,
 0.15320566227007784: 2,
 0.14237122247442516: 41,
 0.13361876347992124: 37,
 0.13214434572825728: 21,
 0.13138217928069298: 46,
 0.1305883354450262: 20,
 0.11450274389491166: 54,
 0.10241985389025904: 15,
 0.09670547870338363: 55,
 0.0954111730929797: 17,
 0.0951514593819736: 53,
 0.09018293318435022: 56,
 0.08805880204920088: 32,
 0.08641956143514624: 23,
 0.07994873791412709: 8,
 0.07974256260432372: 44,
 0.07621680318397792: 51,
 0.07206242869588084: 11,
 0.07096197569536573: 18,
 0.06963095713697386: 30,
 0.06157596955931271: 10,
 0.061090290155775266: 52,
 0.05931599892752986: 43,
 0.057183657309260276: 14,
 0.046509500630423466: 22,
 0.04253728118038031: 12,
 0.041080720399442446: 49,
 0.04094086667100821: 39,
 0.03963806654920571: 6,
 0.03757573322200981: 31,
 0.03666622076770962: 34,
 0.03654724598062173: 27,
 0.034417726506711155: 3,
 0.031192166955416645: 9,
 0.026347385450704343: 33,
 0.023613824763336948: 16,
 0.019681918246060356: 35,
 0.018878513271045203: 0,
 0.015771770924882213: 7,
 0.014394900068712795: 28,
 0.001087520048745067: 13,
 0.0010858416414943012: 45,
 0.0005488320381748891: 19,
 0.0: 50}


# filter dataframe by best features
best_cols = df.drop(['state','name'], 1).iloc[:, [0, 5, 6, 42]].columns
best_cols

Index(['pop2000', 'native_2010', 'asian_2010', 'households_speak_other_2019'], dtype='object')


state_list = ['California','Florida','South Dakota','Wyoming']
df = merged_data2_scaled[merged_data2_scaled['state'].isin(state_list)]
df = df.groupby(['state', 'name'], as_index=False).mean()

# numeric_features= df.select_dtypes(include='number')
X = df[best_cols]

pca = PCA(n_components=2)
components = pca.fit_transform(X)

fig = px.scatter(components, x=0, y=1, color=df['state'], 
                 labels={
                     "0": "PC1",
                     "1": "PC2",
                 },
                title="County Clusters by States")
fig.show()


print('Explained variation per principal component: {}'.format(pca.explained_variance_ratio_))

Explained variation per principal component: [0.47482906 0.34639272]


worst_cols = df.drop(['state','name'], 1).iloc[:, [19, 28, 50]].columns


worst_cols

Index(['mean_work_travel_2010',
       'percent_change_private_nonfarm_employment_2009', 'Year'],
      dtype='object')


# df = merged_data2_scaled
state_list = ['California','Florida','South Dakota','Wyoming']
df = merged_data2_scaled[merged_data2_scaled['state'].isin(state_list)]
df = df.groupby(['state', 'name'], as_index=False).mean()

# numeric_features= df.select_dtypes(include='number')
X = df[worst_cols]

pca = PCA(n_components=2)
components = pca.fit_transform(X)

fig = px.scatter(components, x=0, y=1, color=df['state'], 
                 labels={
                     "0": "PC1",
                     "1": "PC2",
                 },
                title="County Clusters by States")
fig.show()


print('Explained variation per principal component: {}'.format(pca.explained_variance_ratio_))

Explained variation per principal component: [0.60102752 0.39897248]


X = df[best_cols]
y = df['state']
#
# Instantiate the KMeans models
#
km = KMeans(n_clusters=4, random_state=42)
#
# Fit the KMeans model
#
km.fit_predict(X)

#
# Calculate Silhoutte Score
#
score = silhouette_score(X, km.labels_, metric='euclidean')
#
# Print the score
#
print('Silhouetter Score: %.3f' % score)

Silhouetter Score: 0.787


X = df[worst_cols]
y = df['state']
#
# Instantiate the KMeans models
#
km = KMeans(n_clusters=4, random_state=42)
#
# Fit the KMeans model
#
km.fit_predict(X)

#
# Calculate Silhoutte Score
#
score = silhouette_score(X, km.labels_, metric='euclidean')
#
# Print the score
#
print('Silhouetter Score: %.3f' % score)

Silhouetter Score: 0.384


state_list = ['California','Florida','South Dakota','Wyoming']
df = merged_data2_scaled[merged_data2_scaled['state'].isin(state_list)]
df = df.groupby(['state', 'name'], as_index=False).mean()

numeric_features= df.select_dtypes(include='number')
X = df[numeric_features.columns]

embedding = Isomap(n_components=2)
X_transformed = embedding.fit_transform(X)

fig = px.scatter(X_transformed, x=0, y=1, color=df['state'], 
                 labels={
                     "0": "PC1",
                     "1": "PC2",
                 },
                title="County Clusters by States")
fig.show()


# logistic regression for feature importance
from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression
from matplotlib import pyplot

# define the model
model = LogisticRegression()
# fit the model
model.fit(X, df['state'])
# get importance
importance = model.coef_[0]
# summarize feature importance
feat_score = {}
for i,v in enumerate(importance):
# 	print('Feature: %0d, Score: %.5f' % (i,v))
    feat_score[i] = v
# plot feature importance
pyplot.bar([x for x in range(len(importance))], importance)
pyplot.show()


dict(sorted(feat_score.items(), key=lambda item: item[1], reverse=True))

{37: 0.7601477448524282,
 23: 0.6649148016158248,
 10: 0.6102023739133594,
 34: 0.432604931534568,
 16: 0.33388404840454267,
 11: 0.3266526438635486,
 6: 0.3157828307154359,
 12: 0.28167915532322674,
 9: 0.2702311727918338,
 8: 0.2576433497567219,
 47: 0.22413362340385196,
 48: 0.20901851008875003,
 19: 0.2053284954474627,
 7: 0.19407845647192543,
 0: 0.16419006916996712,
 13: 0.12875212131681188,
 2: 0.12010996808797697,
 51: 0.09530764299750079,
 45: 0.09133880604224999,
 55: 0.08797891672800315,
 41: 0.07878270985647758,
 56: 0.05345339508218621,
 53: 0.050193752852760044,
 25: 0.0450820454347938,
 31: 0.034189770191318226,
 46: 0.0296638379315065,
 5: 0.025701793404967203,
 36: 0.017946071537840454,
 44: 0.008216609363549699,
 24: 0.007197245211149242,
 52: 0.0012062215537009418,
 50: 0.0,
 35: -0.0004893800485823716,
 29: -0.013364432187920857,
 20: -0.02372655307270263,
 43: -0.03335935806948628,
 49: -0.040581606473141726,
 26: -0.04302885514705587,
 33: -0.061570872226172586,
 14: -0.0625957899900142,
 54: -0.09449095872040127,
 3: -0.09905588149635984,
 4: -0.10000385888130958,
 42: -0.10142292912010131,
 22: -0.10668966501167992,
 32: -0.1151896645407766,
 40: -0.12275799418514033,
 17: -0.14366154233323206,
 15: -0.1523124688370092,
 21: -0.15891876759132334,
 39: -0.16957208035887555,
 28: -0.1757923579774233,
 1: -0.1786958674102034,
 30: -0.18442338660127844,
 27: -0.21721736398000957,
 18: -0.2608138882428683,
 38: -0.3408574659810999}


best_cols_iso = df.drop(['state','name'], 1).iloc[:, [37,23,10,34,16]].columns


state_list = ['California','Florida','South Dakota','Wyoming']
df = merged_data2_scaled[merged_data2_scaled['state'].isin(state_list)]
df = df.groupby(['state', 'name'], as_index=False).mean()

numeric_features= df.select_dtypes(include='number')
X = df[best_cols_iso]

embedding = Isomap(n_components=2)
X_transformed1 = embedding.fit_transform(X)

fig = px.scatter(X_transformed1, x=0, y=1, color=df['state'], 
                 labels={
                     "0": "PC1",
                     "1": "PC2",
                 },
                title="County Clusters by States")
fig.show()


worst_cols_iso = df.drop(['state','name'], 1).iloc[:, [18,27,38]].columns


state_list = ['California','Florida','South Dakota','Wyoming']
df = merged_data2_scaled[merged_data2_scaled['state'].isin(state_list)]
df = df.groupby(['state', 'name'], as_index=False).mean()

numeric_features= df.select_dtypes(include='number')
X = df[worst_cols_iso]

embedding = Isomap(n_components=2)
X_transformed2 = embedding.fit_transform(X)

fig = px.scatter(X_transformed2, x=0, y=1, color=df['state'], 
                 labels={
                     "0": "PC1",
                     "1": "PC2",
                 },
                title="County Clusters by States")
fig.show()


# Instantiate the KMeans models
km = KMeans(n_clusters=4, random_state=RSEED)

# Fit the KMeans model
km.fit_predict(X_transformed1)

#
# Calculate Silhoutte Score
#
score = silhouette_score(X_transformed1, km.labels_, metric='euclidean')
#
# Print the score
#
print('Silhouetter Score: %.3f' % score)

Silhouetter Score: 0.580


# Instantiate the KMeans models
km = KMeans(n_clusters=4, random_state=RSEED)

# Fit the KMeans model
km.fit_predict(X_transformed2)

#
# Calculate Silhoutte Score
#
score = silhouette_score(X_transformed2, km.labels_, metric='euclidean')
#
# Print the score
#
print('Silhouetter Score: %.3f' % score)

Silhouetter Score: 0.574


house_elections.isnull().sum()

year              0
state             0
office            0
district          0
candidate         0
party             0
candidatevotes    0
totalvotes        0
dtype: int64


year_list = [2010, 2012]

filtered_df = house_elections[house_elections['year'].isin(year_list)]
filtered_df


# total votes
filtered_df = filtered_df.groupby(['year', 'state'], as_index=False)['candidatevotes'].sum()
filtered_df


filtered_df.rename(columns = {'candidatevotes':'totalVotes'}, inplace = True)


filtered_df


county_complete.sample(5, random_state=RSEED)


county_complete['pop2010'].isnull().values.any()

False


county_complete['age_under_18_2010'].isnull().values.any()

False


# VAP for 2010
county_complete['VAP2010'] = np.floor(county_complete['pop2010'] * ((100 - county_complete['age_under_18_2010']) / 100))


county_complete['VAP2010']

0        39945.0
1       140344.0
2        21443.0
3        17713.0
4        43220.0
          ...   
3137     31934.0
3138     17226.0
3139     14740.0
3140      6357.0
3141      5636.0
Name: VAP2010, Length: 3142, dtype: float64


county_complete['age_over_18_2019'].isnull().values.any()

False


# Calculate VAP for 2012
county_complete['VAP2012'] = ((county_complete['age_over_18_2019'] - (100 - county_complete['age_under_18_2010'])) / 10 * 2) + 100 - county_complete['age_under_18_2010']
county_complete['VAP2012'] = county_complete['VAP2012'] / 100
county_complete['VAP2012'] = np.floor(county_complete['VAP2012'] * county_complete['pop2012'])


county_complete['VAP2012']

0        40536.0
1       146831.0
2        21278.0
3        17609.0
4        43587.0
          ...   
3137     32889.0
3138     17562.0
3139     14699.0
3140      6288.0
3141      5543.0
Name: VAP2012, Length: 3142, dtype: float64


county_complete2 = county_complete.groupby(['state'], as_index=False)['VAP2010', 'VAP2012'].sum()
county_complete2 = county_complete2[county_complete2['state'] != 'District of Columbia'].reset_index()


county_complete2 = county_complete2.sort_values(by=['state'])
county_complete2 = county_complete2.drop('index', 1)
county_complete2


df_2010 = filtered_df[filtered_df['year'] == 2010]
df_2010


df_2010 = df_2010.sort_values(by=['state'])
df_2010


df_2010['voter_turnout'] = df_2010['totalVotes'] / county_complete2['VAP2010']
df_2010


df_2012 = filtered_df[filtered_df['year'] == 2012]
df_2012


df_2012 = df_2012.sort_values(by=['state']).reset_index()
df_2012 = df_2012.drop('index', 1)
df_2012


df_2012['voter_turnout'] = df_2012['totalVotes'] / county_complete2['VAP2012']
df_2012


df = house_elections.groupby(['year', 'state'], as_index=False)['candidatevotes'].sum()
df


df.rename(columns = {'candidatevotes':'totalVotes'}, inplace = True)
df


features6 = county_complete.copy()
features6


features6 = features6.groupby('state', as_index=False)['pop2000', 'pop2010', 'pop2012', 'pop2014', 'pop2016'].sum()
# features6 = features6.drop('District of Columbia', 1)
features6


features6 = features6[features6['state'] != 'District of Columbia'].reset_index()
features6


df['population'] = 0
df


df.loc[df['year'] == 2000, 'population'] = features6['pop2000'].values
df.loc[df['year'] == 2010, 'population'] = features6['pop2010'].values
df.loc[df['year'] == 2012, 'population'] = features6['pop2012'].values
df.loc[df['year'] == 2014, 'population'] = features6['pop2014'].values
df.loc[df['year'] == 2016, 'population'] = features6['pop2016'].values


df[df['year']==2000]


((features6['pop2012'] / features6['pop2010']).mean() - 1) * 100

1.5146648329240398


i = 1998
tmp = features6['pop2000'].values
while i >= 1976:
    tmp = tmp * (1-0.015)
    df.loc[df['year'] == i, 'population'] = np.floor(tmp)
    i = i - 2

df


i = 2002
tmp = features6['pop2000'].values
while i < 2010:
    tmp = tmp * (1+0.015)
    df.loc[df['year'] == i, 'population'] = np.floor(tmp)
    i = i + 2


df[(df['year'] >= 2002) & (df['year'] < 2010)]


df = df[df['state'] != 'DISTRICT OF COLUMBIA']
df[df['year'] ==2020]


i = 2018
tmp = features6['pop2016'].values
while i <= 2020:
    tmp = tmp * (1+0.015)
    df.loc[df['year'] == i, 'population'] = np.floor(tmp)
    i = i + 2

df


df['VAP'] = 0


df.loc[df['year'] == 2010, 'VAP'] = county_complete2['VAP2010'].values
df.loc[df['year'] == 2012, 'VAP'] = county_complete2['VAP2012'].values


(df.loc[df['year'] == 2012, 'VAP'].values / df.loc[df['year'] == 2010, 'VAP'].values).mean()

1.0185722751685544


i = 2008
tmp = county_complete2['VAP2010'].values
while i >= 1976:
    tmp = tmp * (1-0.018)
    df.loc[df['year'] == i, 'VAP'] = np.floor(tmp)
    i = i - 2

df


i = 2014
tmp = county_complete2['VAP2012'].values
while i <= 2020:
    tmp = tmp * (1+0.018)
    df.loc[df['year'] == i, 'VAP'] = np.floor(tmp)
    i = i + 2

df


df['can_vote_percent'] = df['VAP'] / df['population']


df['voter_turnout'] = df['totalVotes'] / df['VAP']

df


county_complete['female_num'] = np.floor(county_complete['pop2010'] * (county_complete['female_2010'] / 100))
county_complete


county_complete3 = county_complete.groupby('state',as_index=False)['female_num'].sum()
county_complete3 = county_complete3[county_complete3['state'] != 'District of Columbia'].reset_index()
county_complete3


county_complete3['female_percent'] = county_complete3['female_num'] / features6['pop2010']
county_complete3


df['female_percent'] = 0


i = 1976
tmp = county_complete3['female_percent'].values
while i <= 2020:
    df.loc[df['year'] == i, 'female_percent'] = tmp
    i = i + 2

df


county_complete['bachelors_sum_2017'] = np.floor(county_complete['pop2017'] * (county_complete['bachelors_2017'] / 100))
county_complete['bachelors_sum_2016'] = np.floor(county_complete['pop2016'] * (county_complete['bachelors_2016'] / 100))
county_complete


county_complete4 = county_complete.groupby('state',as_index=False)['bachelors_sum_2016','bachelors_sum_2017', 'pop2017', 'pop2016'].sum()
county_complete4 = county_complete4[county_complete4['state'] != 'District of Columbia'].reset_index()
county_complete4


county_complete4['bachelors_percent_2016'] = county_complete4['bachelors_sum_2016'] / county_complete4['pop2016']
county_complete4['bachelors_percent_2017'] = county_complete4['bachelors_sum_2017'] / county_complete4['pop2017']
county_complete4


(county_complete4['bachelors_percent_2017'] - county_complete4['bachelors_percent_2016']).mean()

0.005774049488857494


df['bachelors_percent'] = 0


i = 2016
tmp = county_complete4['bachelors_percent_2016'].values
while i >= 1976:
    if i != 2016:
        tmp = tmp * (1-0.005)
    df.loc[df['year'] == i, 'bachelors_percent'] = tmp
    i = i - 2


i = 2018
tmp = county_complete4['bachelors_percent_2016'].values
while i <= 2020:
    tmp = tmp * (1+0.005)
    df.loc[df['year'] == i, 'bachelors_percent'] = tmp
    i = i + 2

df


train_data = df[(df['year'] != 2010) & (df['year'] != 2012)]
test_data_2010 = df[df['year'] == 2010]
test_data_2012 = df[df['year'] == 2012]


# split dataset to features and label
X_train = train_data[['totalVotes', 'population', 'VAP', 'can_vote_percent', 'female_percent', 'bachelors_percent']]
y_train = train_data['voter_turnout']
X_test_2010 = test_data_2010[['totalVotes', 'population', 'VAP', 'can_vote_percent', 'female_percent', 'bachelors_percent']]
y_test_2010 = test_data_2010['voter_turnout']
X_test_2012 = test_data_2012[['totalVotes', 'population', 'VAP', 'can_vote_percent', 'female_percent', 'bachelors_percent']]
y_test_2012 = test_data_2012['voter_turnout']


# define model
ab = AdaBoostRegressor(random_state=RSEED)

# define parameter grid
parameters_grid = {
    'n_estimators': [50, 100, 200, 300],
    'learning_rate':[0.01, 0.1, 1.0]
}

# define grid search
grid_search = GridSearchCV(estimator=ab, param_grid=parameters_grid, cv=10)


# fit estimator
grid_search.fit(X_train, y_train)

# get best estimator
best = grid_search.best_estimator_


# print best parameters
pd.DataFrame.from_dict(grid_search.best_params_, orient='index', columns=['Selected Value']).T


# predict on test data 2010
y_pred_2010 = best.predict(X_test_2010)


# calculate MSE, MAE, RMSE and R Squared for train data
MSE = round(mean_squared_error(y_test_2010, y_pred_2010), 3)
MAE = round(mean_absolute_error(y_test_2010,y_pred_2010), 3)
RMSE = round(np.sqrt(mean_squared_error(y_test_2010,y_pred_2010)), 3)
R2 = round(r2_score(y_test_2010,y_pred_2010), 3)

df2 = pd.DataFrame([MSE, MAE, RMSE, R2]).T
df2 = df2.rename(index={0: 'AdaBoost Regressor'}, columns={0: 'MSE', 1:'MAE', 2:'RMSE', 3:"R2"})
df2


tmp2010 = df[df['year'] == 2010]
tmp2010


tmp2010['predicted_turnout'] = y_pred_2010
state_list = ['CALIFORNIA','FLORIDA','SOUTH DAKOTA','WYOMING']

tmp2010 = tmp2010[tmp2010['state'].isin(state_list)]
tmp2010


# plot the dataframe
tmp2010.plot(x="state", y=["voter_turnout", "predicted_turnout"], kind="bar", figsize=(9, 8))
 
# print bar graph
plt.title("Predicted Vs Actual - 2010")
plt.show()


# Create the visualizer, fit, score, and show it
viz = residuals_plot(AdaBoostRegressor(random_state=RSEED, learning_rate=1.0, n_estimators=300), X_train, y_train, X_test_2010, y_test_2010)


visualizer = PredictionError(AdaBoostRegressor(random_state=RSEED, learning_rate=1.0, n_estimators=300))

visualizer.fit(X_train, y_train)  # Fit the training data to the visualizer
visualizer.score(X_test_2010, y_test_2010)  # Evaluate the model on the test data
visualizer.show()

<AxesSubplot:title={'center':'Prediction Error for AdaBoostRegressor'}, xlabel='$y$', ylabel='$\\hat{y}$'>


states5 = df[df['year'] == 2010]
states5['predicted_turnout'] = y_pred_2010
states5['abs_diff'] = abs(states5['voter_turnout'] - states5['predicted_turnout'])
states5.sort_values('abs_diff', ascending=False)


# predict on test data 2012
y_pred_2012 = best.predict(X_test_2012)


# calculate MSE, MAE, RMSE and R Squared for test data
MSE = round(mean_squared_error(y_test_2012, y_pred_2012), 3)
MAE = round(mean_absolute_error(y_test_2012,y_pred_2012), 3)
RMSE = round(np.sqrt(mean_squared_error(y_test_2012,y_pred_2012)), 3)
R2 = round(r2_score(y_test_2012,y_pred_2012), 3)

df2 = pd.DataFrame([MSE, MAE, RMSE, R2]).T
df2 = df2.rename(index={0: 'AdaBoost Regressor'}, columns={0: 'MSE', 1:'MAE', 2:'RMSE', 3:"R2"})
df2


tmp2012 = df[df['year'] == 2012]
tmp2012


tmp2012['predicted_turnout'] = y_pred_2012
state_list = ['CALIFORNIA','FLORIDA','SOUTH DAKOTA','WYOMING']

tmp2012 = tmp2012[tmp2012['state'].isin(state_list)]
tmp2012


# plot the dataframe
tmp2012.plot(x="state", y=["voter_turnout", "predicted_turnout"], kind="bar", figsize=(9, 8))
 
# print bar graph
plt.title("Predicted Vs Actual - 2012")
plt.show()


# Create the visualizer, fit, score, and show it
viz = residuals_plot(AdaBoostRegressor(random_state=RSEED, learning_rate=1.0, n_estimators=300), X_train, y_train, X_test_2012, y_test_2012)


visualizer = PredictionError(AdaBoostRegressor(random_state=RSEED, learning_rate=1.0, n_estimators=300))

visualizer.fit(X_train, y_train)  # Fit the training data to the visualizer
visualizer.score(X_test_2012, y_test_2012)  # Evaluate the model on the test data
visualizer.show()

<AxesSubplot:title={'center':'Prediction Error for AdaBoostRegressor'}, xlabel='$y$', ylabel='$\\hat{y}$'>


states5 = df[df['year'] == 2012]
states5['predicted_turnout'] = y_pred_2012
states5['abs_diff'] = abs(states5['voter_turnout'] - states5['predicted_turnout'])
states5.sort_values('abs_diff', ascending=False)


# get feature importance
imp = pd.DataFrame(grid_search.best_estimator_.fit(X_train, y_train).feature_importances_, 
                   index=X_train.columns, columns=['Importance']).sort_values('Importance')


imp = imp.nlargest(6, 'Importance')
imp


# plot feature importance
plt.figure(figsize=(8, 6))
plt.barh(range(0, len(imp)), imp['Importance'], color='#9ecae1')
plt.grid(axis='x', alpha=0.5, color='lightgrey')
plt.yticks(range(0, len(imp)), imp.index)
plt.title('Feature Importance', fontsize=14)
plt.show()


# define model
dt = DecisionTreeRegressor(random_state=RSEED)

# define parameter grid
parameters_grid = {
    'max_depth': [2, 4, 6, 8, 10],
    'min_samples_split': [2, 4, 6, 8, 10],
    'min_samples_leaf': [2, 4, 6, 8, 10]
}


# define grid search
grid_search = GridSearchCV(estimator=dt, param_grid=parameters_grid, cv=10)


# fit estimator
grid_search.fit(X_train, y_train)

# get best estimator
best = grid_search.best_estimator_


# print best parameters
pd.DataFrame.from_dict(grid_search.best_params_, orient='index', columns=['Selected Value']).T


# predict on test data 2010
y_pred_2010 = best.predict(X_test_2010)


# calculate MSE, MAE, RMSE and R Squared for train data
MSE = round(mean_squared_error(y_test_2010, y_pred_2010), 3)
MAE = round(mean_absolute_error(y_test_2010,y_pred_2010), 3)
RMSE = round(np.sqrt(mean_squared_error(y_test_2010,y_pred_2010)), 3)
R2 = round(r2_score(y_test_2010,y_pred_2010), 3)

df2 = pd.DataFrame([MSE, MAE, RMSE, R2]).T
df2 = df2.rename(index={0: 'AdaBoost Regressor'}, columns={0: 'MSE', 1:'MAE', 2:'RMSE', 3:"R2"})
df2


tmp2010 = df[df['year'] == 2010]
tmp2010


tmp2010['predicted_turnout'] = y_pred_2010
state_list = ['CALIFORNIA','FLORIDA','SOUTH DAKOTA','WYOMING']

tmp2010 = tmp2010[tmp2010['state'].isin(state_list)]
tmp2010


# plot the dataframe
tmp2010.plot(x="state", y=["voter_turnout", "predicted_turnout"], kind="bar", figsize=(9, 8))
 
# print bar graph
plt.title("Predicted Vs Actual - 2010")
plt.show()


# Create the visualizer, fit, score, and show it
viz = residuals_plot(DecisionTreeRegressor(max_depth = 10, min_samples_leaf = 2, min_samples_split = 2), X_train, y_train, X_test_2010, y_test_2010)


visualizer = PredictionError(DecisionTreeRegressor(max_depth = 10, min_samples_leaf = 2, min_samples_split = 2))

visualizer.fit(X_train, y_train)  # Fit the training data to the visualizer
visualizer.score(X_test_2010, y_test_2010)  # Evaluate the model on the test data
visualizer.show()

<AxesSubplot:title={'center':'Prediction Error for DecisionTreeRegressor'}, xlabel='$y$', ylabel='$\\hat{y}$'>


states5 = df[df['year'] == 2010]
states5['predicted_turnout'] = y_pred_2010
states5['abs_diff'] = abs(states5['voter_turnout'] - states5['predicted_turnout'])
states5.sort_values('abs_diff', ascending=False)


# predict on test data 2012
y_pred_2012 = best.predict(X_test_2012)


# calculate MSE, MAE, RMSE and R Squared for test data
MSE = round(mean_squared_error(y_test_2012, y_pred_2012), 3)
MAE = round(mean_absolute_error(y_test_2012,y_pred_2012), 3)
RMSE = round(np.sqrt(mean_squared_error(y_test_2012,y_pred_2012)), 3)
R2 = round(r2_score(y_test_2012,y_pred_2012), 3)

df2 = pd.DataFrame([MSE, MAE, RMSE, R2]).T
df2 = df2.rename(index={0: 'AdaBoost Regressor'}, columns={0: 'MSE', 1:'MAE', 2:'RMSE', 3:"R2"})
df2


tmp2012 = df[df['year'] == 2012]
tmp2012


tmp2012['predicted_turnout'] = y_pred_2012
state_list = ['CALIFORNIA','FLORIDA','SOUTH DAKOTA','WYOMING']

tmp2012 = tmp2012[tmp2012['state'].isin(state_list)]
tmp2012


# plot the dataframe
tmp2012.plot(x="state", y=["voter_turnout", "predicted_turnout"], kind="bar", figsize=(9, 8))
 
# print bar graph
plt.title("Predicted Vs Actual - 2012")
plt.show()


# Create the visualizer, fit, score, and show it
viz = residuals_plot(DecisionTreeRegressor(max_depth = 10, min_samples_leaf = 2, min_samples_split = 2), X_train, y_train, X_test_2012, y_test_2012)


visualizer = PredictionError(DecisionTreeRegressor(max_depth = 10, min_samples_leaf = 2, min_samples_split = 2))

visualizer.fit(X_train, y_train)  # Fit the training data to the visualizer
visualizer.score(X_test_2012, y_test_2012)  # Evaluate the model on the test data
visualizer.show()

<AxesSubplot:title={'center':'Prediction Error for DecisionTreeRegressor'}, xlabel='$y$', ylabel='$\\hat{y}$'>


states5 = df[df['year'] == 2012]
states5['predicted_turnout'] = y_pred_2012
states5['abs_diff'] = abs(states5['voter_turnout'] - states5['predicted_turnout'])
states5.sort_values('abs_diff', ascending=False)


# get feature importance
imp = pd.DataFrame(grid_search.best_estimator_.fit(X_train, y_train).feature_importances_, 
                   index=X_train.columns, columns=['Importance']).sort_values('Importance')


imp = imp.nlargest(6, 'Importance')
imp


# plot feature importance
plt.figure(figsize=(8, 6))
plt.barh(range(0, len(imp)), imp['Importance'], color='#9ecae1')
plt.grid(axis='x', alpha=0.5, color='lightgrey')
plt.yticks(range(0, len(imp)), imp.index)
plt.title('Feature Importance', fontsize=14)
plt.show()


# define model
rf = RandomForestRegressor(random_state=RSEED)

# define parameter grid
parameters_grid = {
    'max_depth': [2, 3, 4, 5, 6],
    'min_samples_split': [2, 4],
    'min_samples_leaf': [2, 8],
    'n_estimators': [20, 50, 100, 200, 500]
}

# define grid search
grid_search = GridSearchCV(estimator=rf, param_grid=parameters_grid, cv=10)


# fit estimator
grid_search.fit(X_train, y_train)

# get best estimator
best = grid_search.best_estimator_


# print best parameters
pd.DataFrame.from_dict(grid_search.best_params_, orient='index', columns=['Selected Value']).T


# predict on test data 2010
y_pred_2010 = best.predict(X_test_2010)


# calculate MSE, MAE, RMSE and R Squared for train data
MSE = round(mean_squared_error(y_test_2010, y_pred_2010), 3)
MAE = round(mean_absolute_error(y_test_2010,y_pred_2010), 3)
RMSE = round(np.sqrt(mean_squared_error(y_test_2010,y_pred_2010)), 3)
R2 = round(r2_score(y_test_2010,y_pred_2010), 3)

df2 = pd.DataFrame([MSE, MAE, RMSE, R2]).T
df2 = df2.rename(index={0: 'AdaBoost Regressor'}, columns={0: 'MSE', 1:'MAE', 2:'RMSE', 3:"R2"})
df2


tmp2010 = df[df['year'] == 2010]
tmp2010


tmp2010['predicted_turnout'] = y_pred_2010
state_list = ['CALIFORNIA','FLORIDA','SOUTH DAKOTA','WYOMING']

tmp2010 = tmp2010[tmp2010['state'].isin(state_list)]
tmp2010


# plot the dataframe
tmp2010.plot(x="state", y=["voter_turnout", "predicted_turnout"], kind="bar", figsize=(9, 8))
 
# print bar graph
plt.title("Predicted Vs Actual - 2010")
plt.show()


# Create the visualizer, fit, score, and show it
viz = residuals_plot(RandomForestRegressor(max_depth = 6, min_samples_leaf = 2, min_samples_split = 2, n_estimators = 500), X_train, y_train, X_test_2010, y_test_2010)


visualizer = PredictionError(RandomForestRegressor(max_depth = 6, min_samples_leaf = 2, min_samples_split = 2, n_estimators = 500))

visualizer.fit(X_train, y_train)  # Fit the training data to the visualizer
visualizer.score(X_test_2010, y_test_2010)  # Evaluate the model on the test data
visualizer.show()

<AxesSubplot:title={'center':'Prediction Error for RandomForestRegressor'}, xlabel='$y$', ylabel='$\\hat{y}$'>


states5 = df[df['year'] == 2010]
states5['predicted_turnout'] = y_pred_2010
states5['abs_diff'] = abs(states5['voter_turnout'] - states5['predicted_turnout'])
states5.sort_values('abs_diff', ascending=False)


# predict on test data 2012
y_pred_2012 = best.predict(X_test_2012)


# calculate MSE, MAE, RMSE and R Squared for test data
MSE = round(mean_squared_error(y_test_2012, y_pred_2012), 3)
MAE = round(mean_absolute_error(y_test_2012,y_pred_2012), 3)
RMSE = round(np.sqrt(mean_squared_error(y_test_2012,y_pred_2012)), 3)
R2 = round(r2_score(y_test_2012,y_pred_2012), 3)

df2 = pd.DataFrame([MSE, MAE, RMSE, R2]).T
df2 = df2.rename(index={0: 'AdaBoost Regressor'}, columns={0: 'MSE', 1:'MAE', 2:'RMSE', 3:"R2"})
df2


tmp2012 = df[df['year'] == 2012]
tmp2012


tmp2012['predicted_turnout'] = y_pred_2012
state_list = ['CALIFORNIA','FLORIDA','SOUTH DAKOTA','WYOMING']

tmp2012 = tmp2012[tmp2012['state'].isin(state_list)]
tmp2012


# plot the dataframe
tmp2012.plot(x="state", y=["voter_turnout", "predicted_turnout"], kind="bar", figsize=(9, 8))
 
# print bar graph
plt.title("Predicted Vs Actual - 2012")
plt.show()


# Create the visualizer, fit, score, and show it
viz = residuals_plot(RandomForestRegressor(max_depth = 6, min_samples_leaf = 2, min_samples_split = 2, n_estimators = 500), X_train, y_train, X_test_2012, y_test_2012)


visualizer = PredictionError(RandomForestRegressor(max_depth = 6, min_samples_leaf = 2, min_samples_split = 2, n_estimators = 500))

visualizer.fit(X_train, y_train)  # Fit the training data to the visualizer
visualizer.score(X_test_2012, y_test_2012)  # Evaluate the model on the test data
visualizer.show()

<AxesSubplot:title={'center':'Prediction Error for RandomForestRegressor'}, xlabel='$y$', ylabel='$\\hat{y}$'>


states5 = df[df['year'] == 2012]
states5['predicted_turnout'] = y_pred_2012
states5['abs_diff'] = abs(states5['voter_turnout'] - states5['predicted_turnout'])
states5.sort_values('abs_diff', ascending=False)


# get feature importance
imp = pd.DataFrame(grid_search.best_estimator_.fit(X_train, y_train).feature_importances_, 
                   index=X_train.columns, columns=['Importance']).sort_values('Importance')


imp = imp.nlargest(6, 'Importance')
imp


# plot feature importance
plt.figure(figsize=(8, 6))
plt.barh(range(0, len(imp)), imp['Importance'], color='#9ecae1')
plt.grid(axis='x', alpha=0.5, color='lightgrey')
plt.yticks(range(0, len(imp)), imp.index)
plt.title('Feature Importance', fontsize=14)
plt.show()


house_elections


grouped_data = house_elections.groupby(['year', 'state', 'party'], as_index=False)['candidatevotes'].sum()
grouped_data


grouped_data = grouped_data.loc[grouped_data.groupby(["year", "state"])["candidatevotes"].idxmax()]


grouped_data


grouped_data.loc[grouped_data['party'] == 'DEMOCRAT', 'state_class'] = 'D'
grouped_data.loc[grouped_data['party'] == 'REPUBLICAN', 'state_class'] = 'R'
grouped_data = grouped_data.drop('party', 1)


grouped_data = grouped_data[grouped_data['state'] != 'DISTRICT OF COLUMBIA'].reset_index()


grouped_data

df


grouped_data['VAP'] = df['VAP']
grouped_data['female_percent'] = df['female_percent']
grouped_data['bachelors_percent'] = df['bachelors_percent']
grouped_data['can_vote_percent'] = df['can_vote_percent']
grouped_data['voter_turnout'] = df['voter_turnout']


grouped_data = grouped_data.drop('index', 1)


grouped_data.isnull().sum()

year                  0
state                 0
candidatevotes        0
state_class          17
VAP                   1
female_percent        1
bachelors_percent     1
can_vote_percent      1
voter_turnout         1
dtype: int64


grouped_data = grouped_data.dropna(how='any',axis=0)


grouped_data.isnull().sum()

year                 0
state                0
candidatevotes       0
state_class          0
VAP                  0
female_percent       0
bachelors_percent    0
can_vote_percent     0
voter_turnout        0
dtype: int64


train_data = grouped_data[(grouped_data['year'] != 2010) & (grouped_data['year'] != 2012)]
test_data_2010 = grouped_data[grouped_data['year'] == 2010]
test_data_2012 = grouped_data[grouped_data['year'] == 2012]


# split dataset to features and label
X_train = train_data[['candidatevotes', 'VAP', 'can_vote_percent', 'female_percent', 'bachelors_percent', 'voter_turnout']]
y_train = train_data['state_class']
X_test_2010 = test_data_2010[['candidatevotes', 'VAP', 'can_vote_percent', 'female_percent', 'bachelors_percent', 'voter_turnout']]
y_test_2010 = test_data_2010['state_class']
X_test_2012 = test_data_2012[['candidatevotes', 'VAP', 'can_vote_percent', 'female_percent', 'bachelors_percent', 'voter_turnout']]
y_test_2012 = test_data_2012['state_class']


#Normalize the data
mapper = DataFrameMapper([(X_train.columns, StandardScaler())])
scaled_features = mapper.fit_transform(X_train.copy(), 4)
X_train_scaled = pd.DataFrame(scaled_features, index=X_train.index, columns=X_train.columns)
X_train_scaled


#Normalize the data - 2010
mapper = DataFrameMapper([(X_test_2010.columns, StandardScaler())])
scaled_features = mapper.fit_transform(X_test_2010.copy(), 4)
X_test_scaled_2010 = pd.DataFrame(scaled_features, index=X_test_2010.index, columns=X_test_2010.columns)
X_test_scaled_2010


#Normalize the data - 2010
mapper = DataFrameMapper([(X_test_2012.columns, StandardScaler())])
scaled_features = mapper.fit_transform(X_test_2012.copy(), 4)
X_test_scaled_2012 = pd.DataFrame(scaled_features, index=X_test_2012.index, columns=X_test_2012.columns)
X_test_scaled_2012


# define model
rf = RandomForestClassifier(random_state=RSEED)

# define parameter grid
parameters_grid = {
    'max_depth': [3, 4, 5, 6, 7, 8, 9, 10],
    'min_samples_split': [2, 3, 4],
    'min_samples_leaf': [2, 4, 6],
    'n_estimators': [20, 50, 80, 100]
}

# define grid search
grid_search = GridSearchCV(estimator=rf, param_grid=parameters_grid, cv=10)


# fit estimator
grid_search.fit(X_train_scaled, y_train)

# get best estimator
best = grid_search.best_estimator_


# print best parameters
pd.DataFrame.from_dict(grid_search.best_params_, orient='index', columns=['Selected Value']).T


# predict
y_pred_2010 = best.predict(X_test_scaled_2010)


# calculate metrics
acc = round(accuracy_score(y_test_2010, y_pred_2010), 3)

# display metrics
rf_df = pd.DataFrame([acc]).T
rf_df = rf_df.rename(index={0: 'Random Forest Classifier'}, columns={0: 'Accuracy'})
rf_df


# plot confusion metrics
plot_confusion_matrix(best, X_test_scaled_2010, y_test_2010, cmap='Blues')
plt.title("Random Forest Confusion Matrix")
plt.show()


# predict
y_pred_2012 = best.predict(X_test_scaled_2012)


# calculate metrics
acc = round(accuracy_score(y_test_2012, y_pred_2012), 3)

# display metrics
rf_df = pd.DataFrame([acc]).T
rf_df = rf_df.rename(index={0: 'Random Forest Classifier'}, columns={0: 'Accuracy'})
rf_df


# plot confusion metrics
plot_confusion_matrix(best, X_test_scaled_2012, y_test_2012, cmap='Blues')
plt.title("Random Forest Confusion Matrix")
plt.show()


# get feature importance
imp = pd.DataFrame(grid_search.best_estimator_.fit(X_train, y_train).feature_importances_, 
                   index=X_train.columns, columns=['Importance']).sort_values('Importance')


imp = imp.nlargest(6, 'Importance')
imp


# plot feature importance
plt.figure(figsize=(8, 6))
plt.barh(range(0, len(imp)), imp['Importance'], color='#9ecae1')
plt.grid(axis='x', alpha=0.5, color='lightgrey')
plt.yticks(range(0, len(imp)), imp.index)
plt.title('Feature Importance', fontsize=14)
plt.show()


# define model
gb = GradientBoostingClassifier(random_state=RSEED)

# define parameter grid
parameters_grid = {
    'learning_rate': [0.5, 1, 1.5, 2],
    'n_estimators': [50, 100, 200]

}

# define grid search
grid_search = GridSearchCV(estimator=gb, param_grid=parameters_grid, cv=10)


# fit estimator
grid_search.fit(X_train_scaled, y_train)

# get best estimator
best = grid_search.best_estimator_


# print best parameters
pd.DataFrame.from_dict(grid_search.best_params_, orient='index', columns=['Selected Value']).T


# predict
y_pred_2010 = best.predict(X_test_scaled_2010)


# calculate metrics
acc = round(accuracy_score(y_test_2010, y_pred_2010), 3)

# display metrics
rf_df = pd.DataFrame([acc]).T
rf_df = rf_df.rename(index={0: 'Random Forest Classifier'}, columns={0: 'Accuracy'})
rf_df


# plot confusion metrics
plot_confusion_matrix(best, X_test_scaled_2012, y_test_2012, cmap='Blues')
plt.title("Random Forest Confusion Matrix")
plt.show()


# predict
y_pred_2012 = best.predict(X_test_scaled_2012)


# calculate metrics
acc = round(accuracy_score(y_test_2012, y_pred_2012), 3)

# display metrics
rf_df = pd.DataFrame([acc]).T
rf_df = rf_df.rename(index={0: 'Random Forest Classifier'}, columns={0: 'Accuracy'})
rf_df


# plot confusion metrics
plot_confusion_matrix(best, X_test_scaled_2012, y_test_2012, cmap='Blues')
plt.title("Random Forest Confusion Matrix")
plt.show()


# get feature importance
imp = pd.DataFrame(grid_search.best_estimator_.fit(X_train, y_train).feature_importances_, 
                   index=X_train.columns, columns=['Importance']).sort_values('Importance')


imp = imp.nlargest(6, 'Importance')
imp


# plot feature importance
plt.figure(figsize=(8, 6))
plt.barh(range(0, len(imp)), imp['Importance'], color='#b3cde3')
plt.grid(axis='x', alpha=0.5, color='lightgrey')
plt.yticks(range(0, len(imp)), imp.index)
plt.title('Feature Importance', fontsize=14)
plt.show()


param_grid = {'C': [0.1,1, 10, 100], 'gamma': [1,0.1,0.01,0.001],'kernel': ['rbf', 'poly', 'sigmoid']}


grid = GridSearchCV(SVC(),param_grid,refit=True,verbose=2)
grid.fit(X_train_scaled,y_train)

Fitting 5 folds for each of 48 candidates, totalling 240 fits
[CV] END .........................C=0.1, gamma=1, kernel=rbf; total time=   0.0s
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[CV] END ........................C=1, gamma=0.01, kernel=rbf; total time=   0.0s
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[CV] END .......................C=1, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END .......................C=1, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END .......................C=1, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END .......................C=1, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END .......................C=1, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END ....................C=1, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ....................C=1, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ....................C=1, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ....................C=1, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ....................C=1, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END .......................C=1, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .......................C=1, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .......................C=1, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .......................C=1, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .......................C=1, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END ......................C=1, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END ......................C=1, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END ......................C=1, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END ......................C=1, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END ......................C=1, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END ...................C=1, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=1, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=1, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=1, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=1, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END ..........................C=10, gamma=1, kernel=rbf; total time=   0.0s
[CV] END ..........................C=10, gamma=1, kernel=rbf; total time=   0.0s
[CV] END ..........................C=10, gamma=1, kernel=rbf; total time=   0.0s
[CV] END ..........................C=10, gamma=1, kernel=rbf; total time=   0.0s
[CV] END ..........................C=10, gamma=1, kernel=rbf; total time=   0.0s
[CV] END .........................C=10, gamma=1, kernel=poly; total time=   2.3s
[CV] END .........................C=10, gamma=1, kernel=poly; total time=   3.6s
[CV] END .........................C=10, gamma=1, kernel=poly; total time=   3.3s
[CV] END .........................C=10, gamma=1, kernel=poly; total time=   2.9s
[CV] END .........................C=10, gamma=1, kernel=poly; total time=   2.5s
[CV] END ......................C=10, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END ......................C=10, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END ......................C=10, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END ......................C=10, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END ......................C=10, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END ........................C=10, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END ........................C=10, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END ........................C=10, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END ........................C=10, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END ........................C=10, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END .......................C=10, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END .......................C=10, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END .......................C=10, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END .......................C=10, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END .......................C=10, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END ....................C=10, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END ....................C=10, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END ....................C=10, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END ....................C=10, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END ....................C=10, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END .......................C=10, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END .......................C=10, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END .......................C=10, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END .......................C=10, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END .......................C=10, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ......................C=10, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END ......................C=10, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END ......................C=10, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END ......................C=10, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END ......................C=10, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END ...................C=10, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=10, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=10, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=10, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=10, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ......................C=10, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END ......................C=10, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END ......................C=10, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END ......................C=10, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END ......................C=10, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .....................C=10, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END .....................C=10, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END .....................C=10, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END .....................C=10, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END .....................C=10, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END ..................C=10, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END ..................C=10, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END ..................C=10, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END ..................C=10, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END ..................C=10, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END .........................C=100, gamma=1, kernel=rbf; total time=   0.0s
[CV] END .........................C=100, gamma=1, kernel=rbf; total time=   0.0s
[CV] END .........................C=100, gamma=1, kernel=rbf; total time=   0.0s
[CV] END .........................C=100, gamma=1, kernel=rbf; total time=   0.0s
[CV] END .........................C=100, gamma=1, kernel=rbf; total time=   0.0s
[CV] END ........................C=100, gamma=1, kernel=poly; total time=  24.0s
[CV] END ........................C=100, gamma=1, kernel=poly; total time=  37.4s
[CV] END ........................C=100, gamma=1, kernel=poly; total time= 1.2min
[CV] END ........................C=100, gamma=1, kernel=poly; total time=  35.6s
[CV] END ........................C=100, gamma=1, kernel=poly; total time=  30.2s
[CV] END .....................C=100, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END .....................C=100, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END .....................C=100, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END .....................C=100, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END .....................C=100, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END .......................C=100, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END .......................C=100, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END .......................C=100, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END .......................C=100, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END .......................C=100, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END ......................C=100, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END ......................C=100, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END ......................C=100, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END ......................C=100, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END ......................C=100, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END ...................C=100, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=100, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=100, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=100, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=100, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END ......................C=100, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ......................C=100, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ......................C=100, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ......................C=100, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ......................C=100, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END .....................C=100, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END .....................C=100, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END .....................C=100, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END .....................C=100, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END .....................C=100, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END ..................C=100, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ..................C=100, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ..................C=100, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ..................C=100, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ..................C=100, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END .....................C=100, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .....................C=100, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .....................C=100, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .....................C=100, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .....................C=100, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END ....................C=100, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END ....................C=100, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END ....................C=100, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END ....................C=100, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END ....................C=100, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END .................C=100, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END .................C=100, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END .................C=100, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END .................C=100, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END .................C=100, gamma=0.001, kernel=sigmoid; total time=   0.0s

GridSearchCV(estimator=SVC(),
             param_grid={'C': [0.1, 1, 10, 100], 'gamma': [1, 0.1, 0.01, 0.001],
                         'kernel': ['rbf', 'poly', 'sigmoid']},
             verbose=2)


print(grid.best_estimator_)

SVC(C=10, gamma=1)


grid_predictions = grid.predict(X_test_scaled_2010)
print(confusion_matrix(y_test_2010,grid_predictions))
print(classification_report(y_test_2010,grid_predictions))#Output

[[12  2]
 [12 24]]
              precision    recall  f1-score   support

           D       0.50      0.86      0.63        14
           R       0.92      0.67      0.77        36

    accuracy                           0.72        50
   macro avg       0.71      0.76      0.70        50
weighted avg       0.80      0.72      0.73        50


from sklearn.inspection import permutation_importance
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

grid.fit(X_train_scaled, y_train)

perm_importance = permutation_importance(grid, X_test_scaled_2010, y_test_2010)

feature_names = X_train_scaled.columns
features = np.array(feature_names)

sorted_idx = perm_importance.importances_mean.argsort()
plt.barh(features[sorted_idx], perm_importance.importances_mean[sorted_idx])
plt.xlabel("Permutation Importance")

Fitting 5 folds for each of 48 candidates, totalling 240 fits
[CV] END .........................C=0.1, gamma=1, kernel=rbf; total time=   0.0s
[CV] END .........................C=0.1, gamma=1, kernel=rbf; total time=   0.0s
[CV] END .........................C=0.1, gamma=1, kernel=rbf; total time=   0.0s
[CV] END .........................C=0.1, gamma=1, kernel=rbf; total time=   0.0s
[CV] END .........................C=0.1, gamma=1, kernel=rbf; total time=   0.0s
[CV] END ........................C=0.1, gamma=1, kernel=poly; total time=   0.0s
[CV] END ........................C=0.1, gamma=1, kernel=poly; total time=   0.0s
[CV] END ........................C=0.1, gamma=1, kernel=poly; total time=   0.0s
[CV] END ........................C=0.1, gamma=1, kernel=poly; total time=   0.0s
[CV] END ........................C=0.1, gamma=1, kernel=poly; total time=   0.0s
[CV] END .....................C=0.1, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END .....................C=0.1, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END .....................C=0.1, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END .....................C=0.1, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END .....................C=0.1, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END .......................C=0.1, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END .......................C=0.1, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END .......................C=0.1, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END .......................C=0.1, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END .......................C=0.1, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END ......................C=0.1, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END ......................C=0.1, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END ......................C=0.1, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END ......................C=0.1, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END ......................C=0.1, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END ...................C=0.1, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=0.1, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=0.1, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=0.1, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=0.1, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END ......................C=0.1, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ......................C=0.1, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ......................C=0.1, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ......................C=0.1, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ......................C=0.1, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END .....................C=0.1, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END .....................C=0.1, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END .....................C=0.1, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END .....................C=0.1, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END .....................C=0.1, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END ..................C=0.1, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ..................C=0.1, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ..................C=0.1, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ..................C=0.1, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ..................C=0.1, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END .....................C=0.1, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .....................C=0.1, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .....................C=0.1, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .....................C=0.1, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .....................C=0.1, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END ....................C=0.1, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END ....................C=0.1, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END ....................C=0.1, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END ....................C=0.1, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END ....................C=0.1, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END .................C=0.1, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END .................C=0.1, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END .................C=0.1, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END .................C=0.1, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END .................C=0.1, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END ...........................C=1, gamma=1, kernel=rbf; total time=   0.0s
[CV] END ...........................C=1, gamma=1, kernel=rbf; total time=   0.0s
[CV] END ...........................C=1, gamma=1, kernel=rbf; total time=   0.0s
[CV] END ...........................C=1, gamma=1, kernel=rbf; total time=   0.0s
[CV] END ...........................C=1, gamma=1, kernel=rbf; total time=   0.0s
[CV] END ..........................C=1, gamma=1, kernel=poly; total time=   0.4s
[CV] END ..........................C=1, gamma=1, kernel=poly; total time=   0.3s
[CV] END ..........................C=1, gamma=1, kernel=poly; total time=   0.4s
[CV] END ..........................C=1, gamma=1, kernel=poly; total time=   0.4s
[CV] END ..........................C=1, gamma=1, kernel=poly; total time=   0.1s
[CV] END .......................C=1, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END .......................C=1, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END .......................C=1, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END .......................C=1, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END .......................C=1, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END .........................C=1, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END .........................C=1, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END .........................C=1, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END .........................C=1, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END .........................C=1, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END ........................C=1, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END ........................C=1, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END ........................C=1, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END ........................C=1, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END ........................C=1, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END .....................C=1, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END .....................C=1, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END .....................C=1, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END .....................C=1, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END .....................C=1, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END ........................C=1, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ........................C=1, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ........................C=1, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ........................C=1, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ........................C=1, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END .......................C=1, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END .......................C=1, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END .......................C=1, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END .......................C=1, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END .......................C=1, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END ....................C=1, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ....................C=1, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ....................C=1, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ....................C=1, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ....................C=1, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END .......................C=1, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .......................C=1, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .......................C=1, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .......................C=1, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .......................C=1, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END ......................C=1, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END ......................C=1, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END ......................C=1, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END ......................C=1, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END ......................C=1, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END ...................C=1, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=1, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=1, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=1, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=1, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END ..........................C=10, gamma=1, kernel=rbf; total time=   0.0s
[CV] END ..........................C=10, gamma=1, kernel=rbf; total time=   0.0s
[CV] END ..........................C=10, gamma=1, kernel=rbf; total time=   0.0s
[CV] END ..........................C=10, gamma=1, kernel=rbf; total time=   0.0s
[CV] END ..........................C=10, gamma=1, kernel=rbf; total time=   0.0s
[CV] END .........................C=10, gamma=1, kernel=poly; total time=   2.3s
[CV] END .........................C=10, gamma=1, kernel=poly; total time=   3.7s
[CV] END .........................C=10, gamma=1, kernel=poly; total time=   3.4s
[CV] END .........................C=10, gamma=1, kernel=poly; total time=   3.0s
[CV] END .........................C=10, gamma=1, kernel=poly; total time=   2.6s
[CV] END ......................C=10, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END ......................C=10, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END ......................C=10, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END ......................C=10, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END ......................C=10, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END ........................C=10, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END ........................C=10, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END ........................C=10, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END ........................C=10, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END ........................C=10, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END .......................C=10, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END .......................C=10, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END .......................C=10, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END .......................C=10, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END .......................C=10, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END ....................C=10, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END ....................C=10, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END ....................C=10, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END ....................C=10, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END ....................C=10, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END .......................C=10, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END .......................C=10, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END .......................C=10, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END .......................C=10, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END .......................C=10, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ......................C=10, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END ......................C=10, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END ......................C=10, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END ......................C=10, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END ......................C=10, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END ...................C=10, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=10, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=10, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=10, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=10, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ......................C=10, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END ......................C=10, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END ......................C=10, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END ......................C=10, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END ......................C=10, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .....................C=10, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END .....................C=10, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END .....................C=10, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END .....................C=10, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END .....................C=10, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END ..................C=10, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END ..................C=10, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END ..................C=10, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END ..................C=10, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END ..................C=10, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END .........................C=100, gamma=1, kernel=rbf; total time=   0.0s
[CV] END .........................C=100, gamma=1, kernel=rbf; total time=   0.0s
[CV] END .........................C=100, gamma=1, kernel=rbf; total time=   0.0s
[CV] END .........................C=100, gamma=1, kernel=rbf; total time=   0.0s
[CV] END .........................C=100, gamma=1, kernel=rbf; total time=   0.0s
[CV] END ........................C=100, gamma=1, kernel=poly; total time=  24.8s
[CV] END ........................C=100, gamma=1, kernel=poly; total time=  37.9s
[CV] END ........................C=100, gamma=1, kernel=poly; total time= 1.1min
[CV] END ........................C=100, gamma=1, kernel=poly; total time=  36.7s
[CV] END ........................C=100, gamma=1, kernel=poly; total time=  30.6s
[CV] END .....................C=100, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END .....................C=100, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END .....................C=100, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END .....................C=100, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END .....................C=100, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END .......................C=100, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END .......................C=100, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END .......................C=100, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END .......................C=100, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END .......................C=100, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END ......................C=100, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END ......................C=100, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END ......................C=100, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END ......................C=100, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END ......................C=100, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END ...................C=100, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=100, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=100, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=100, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=100, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END ......................C=100, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ......................C=100, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ......................C=100, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ......................C=100, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ......................C=100, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END .....................C=100, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END .....................C=100, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END .....................C=100, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END .....................C=100, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END .....................C=100, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END ..................C=100, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ..................C=100, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ..................C=100, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ..................C=100, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ..................C=100, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END .....................C=100, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .....................C=100, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .....................C=100, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .....................C=100, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .....................C=100, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END ....................C=100, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END ....................C=100, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END ....................C=100, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END ....................C=100, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END ....................C=100, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END .................C=100, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END .................C=100, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END .................C=100, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END .................C=100, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END .................C=100, gamma=0.001, kernel=sigmoid; total time=   0.0s

Text(0.5, 0, 'Permutation Importance')


grid_predictions = grid.predict(X_test_scaled_2012)
print(confusion_matrix(y_test_2012,grid_predictions))
print(classification_report(y_test_2012,grid_predictions))#Output

[[14  7]
 [10 19]]
              precision    recall  f1-score   support

           D       0.58      0.67      0.62        21
           R       0.73      0.66      0.69        29

    accuracy                           0.66        50
   macro avg       0.66      0.66      0.66        50
weighted avg       0.67      0.66      0.66        50


from sklearn.inspection import permutation_importance
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

grid.fit(X_train_scaled, y_train)

perm_importance = permutation_importance(grid, X_test_scaled_2012, y_test_2012)

feature_names = X_train_scaled.columns
features = np.array(feature_names)

sorted_idx = perm_importance.importances_mean.argsort()
plt.barh(features[sorted_idx], perm_importance.importances_mean[sorted_idx])
plt.xlabel("Permutation Importance")

Fitting 5 folds for each of 48 candidates, totalling 240 fits
[CV] END .........................C=0.1, gamma=1, kernel=rbf; total time=   0.0s
[CV] END .........................C=0.1, gamma=1, kernel=rbf; total time=   0.0s
[CV] END .........................C=0.1, gamma=1, kernel=rbf; total time=   0.0s
[CV] END .........................C=0.1, gamma=1, kernel=rbf; total time=   0.0s
[CV] END .........................C=0.1, gamma=1, kernel=rbf; total time=   0.0s
[CV] END ........................C=0.1, gamma=1, kernel=poly; total time=   0.0s
[CV] END ........................C=0.1, gamma=1, kernel=poly; total time=   0.0s
[CV] END ........................C=0.1, gamma=1, kernel=poly; total time=   0.0s
[CV] END ........................C=0.1, gamma=1, kernel=poly; total time=   0.0s
[CV] END ........................C=0.1, gamma=1, kernel=poly; total time=   0.0s
[CV] END .....................C=0.1, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END .....................C=0.1, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END .....................C=0.1, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END .....................C=0.1, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END .....................C=0.1, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END .......................C=0.1, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END .......................C=0.1, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END .......................C=0.1, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END .......................C=0.1, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END .......................C=0.1, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END ......................C=0.1, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END ......................C=0.1, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END ......................C=0.1, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END ......................C=0.1, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END ......................C=0.1, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END ...................C=0.1, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=0.1, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=0.1, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=0.1, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=0.1, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END ......................C=0.1, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ......................C=0.1, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ......................C=0.1, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ......................C=0.1, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ......................C=0.1, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END .....................C=0.1, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END .....................C=0.1, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END .....................C=0.1, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END .....................C=0.1, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END .....................C=0.1, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END ..................C=0.1, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ..................C=0.1, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ..................C=0.1, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ..................C=0.1, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ..................C=0.1, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END .....................C=0.1, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .....................C=0.1, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .....................C=0.1, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .....................C=0.1, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .....................C=0.1, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END ....................C=0.1, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END ....................C=0.1, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END ....................C=0.1, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END ....................C=0.1, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END ....................C=0.1, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END .................C=0.1, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END .................C=0.1, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END .................C=0.1, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END .................C=0.1, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END .................C=0.1, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END ...........................C=1, gamma=1, kernel=rbf; total time=   0.0s
[CV] END ...........................C=1, gamma=1, kernel=rbf; total time=   0.0s
[CV] END ...........................C=1, gamma=1, kernel=rbf; total time=   0.0s
[CV] END ...........................C=1, gamma=1, kernel=rbf; total time=   0.0s
[CV] END ...........................C=1, gamma=1, kernel=rbf; total time=   0.0s
[CV] END ..........................C=1, gamma=1, kernel=poly; total time=   0.4s
[CV] END ..........................C=1, gamma=1, kernel=poly; total time=   0.3s
[CV] END ..........................C=1, gamma=1, kernel=poly; total time=   0.4s
[CV] END ..........................C=1, gamma=1, kernel=poly; total time=   0.4s
[CV] END ..........................C=1, gamma=1, kernel=poly; total time=   0.1s
[CV] END .......................C=1, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END .......................C=1, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END .......................C=1, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END .......................C=1, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END .......................C=1, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END .........................C=1, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END .........................C=1, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END .........................C=1, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END .........................C=1, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END .........................C=1, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END ........................C=1, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END ........................C=1, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END ........................C=1, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END ........................C=1, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END ........................C=1, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END .....................C=1, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END .....................C=1, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END .....................C=1, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END .....................C=1, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END .....................C=1, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END ........................C=1, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ........................C=1, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ........................C=1, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ........................C=1, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ........................C=1, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END .......................C=1, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END .......................C=1, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END .......................C=1, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END .......................C=1, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END .......................C=1, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END ....................C=1, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ....................C=1, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ....................C=1, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ....................C=1, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ....................C=1, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END .......................C=1, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .......................C=1, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .......................C=1, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .......................C=1, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .......................C=1, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END ......................C=1, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END ......................C=1, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END ......................C=1, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END ......................C=1, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END ......................C=1, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END ...................C=1, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=1, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=1, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=1, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=1, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END ..........................C=10, gamma=1, kernel=rbf; total time=   0.0s
[CV] END ..........................C=10, gamma=1, kernel=rbf; total time=   0.0s
[CV] END ..........................C=10, gamma=1, kernel=rbf; total time=   0.0s
[CV] END ..........................C=10, gamma=1, kernel=rbf; total time=   0.0s
[CV] END ..........................C=10, gamma=1, kernel=rbf; total time=   0.0s
[CV] END .........................C=10, gamma=1, kernel=poly; total time=   2.4s
[CV] END .........................C=10, gamma=1, kernel=poly; total time=   3.7s
[CV] END .........................C=10, gamma=1, kernel=poly; total time=   3.3s
[CV] END .........................C=10, gamma=1, kernel=poly; total time=   2.9s
[CV] END .........................C=10, gamma=1, kernel=poly; total time=   2.6s
[CV] END ......................C=10, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END ......................C=10, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END ......................C=10, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END ......................C=10, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END ......................C=10, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END ........................C=10, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END ........................C=10, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END ........................C=10, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END ........................C=10, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END ........................C=10, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END .......................C=10, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END .......................C=10, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END .......................C=10, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END .......................C=10, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END .......................C=10, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END ....................C=10, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END ....................C=10, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END ....................C=10, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END ....................C=10, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END ....................C=10, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END .......................C=10, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END .......................C=10, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END .......................C=10, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END .......................C=10, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END .......................C=10, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ......................C=10, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END ......................C=10, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END ......................C=10, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END ......................C=10, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END ......................C=10, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END ...................C=10, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=10, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=10, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=10, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=10, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ......................C=10, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END ......................C=10, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END ......................C=10, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END ......................C=10, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END ......................C=10, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .....................C=10, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END .....................C=10, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END .....................C=10, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END .....................C=10, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END .....................C=10, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END ..................C=10, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END ..................C=10, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END ..................C=10, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END ..................C=10, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END ..................C=10, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END .........................C=100, gamma=1, kernel=rbf; total time=   0.0s
[CV] END .........................C=100, gamma=1, kernel=rbf; total time=   0.0s
[CV] END .........................C=100, gamma=1, kernel=rbf; total time=   0.0s
[CV] END .........................C=100, gamma=1, kernel=rbf; total time=   0.0s
[CV] END .........................C=100, gamma=1, kernel=rbf; total time=   0.0s
[CV] END ........................C=100, gamma=1, kernel=poly; total time=  25.3s
[CV] END ........................C=100, gamma=1, kernel=poly; total time=  37.9s
[CV] END ........................C=100, gamma=1, kernel=poly; total time= 1.1min
[CV] END ........................C=100, gamma=1, kernel=poly; total time=  35.0s
[CV] END ........................C=100, gamma=1, kernel=poly; total time=  29.8s
[CV] END .....................C=100, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END .....................C=100, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END .....................C=100, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END .....................C=100, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END .....................C=100, gamma=1, kernel=sigmoid; total time=   0.0s
[CV] END .......................C=100, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END .......................C=100, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END .......................C=100, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END .......................C=100, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END .......................C=100, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END ......................C=100, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END ......................C=100, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END ......................C=100, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END ......................C=100, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END ......................C=100, gamma=0.1, kernel=poly; total time=   0.0s
[CV] END ...................C=100, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=100, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=100, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=100, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END ...................C=100, gamma=0.1, kernel=sigmoid; total time=   0.0s
[CV] END ......................C=100, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ......................C=100, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ......................C=100, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ......................C=100, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ......................C=100, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END .....................C=100, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END .....................C=100, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END .....................C=100, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END .....................C=100, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END .....................C=100, gamma=0.01, kernel=poly; total time=   0.0s
[CV] END ..................C=100, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ..................C=100, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ..................C=100, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ..................C=100, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END ..................C=100, gamma=0.01, kernel=sigmoid; total time=   0.0s
[CV] END .....................C=100, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .....................C=100, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .....................C=100, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .....................C=100, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .....................C=100, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END ....................C=100, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END ....................C=100, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END ....................C=100, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END ....................C=100, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END ....................C=100, gamma=0.001, kernel=poly; total time=   0.0s
[CV] END .................C=100, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END .................C=100, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END .................C=100, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END .................C=100, gamma=0.001, kernel=sigmoid; total time=   0.0s
[CV] END .................C=100, gamma=0.001, kernel=sigmoid; total time=   0.0s

Text(0.5, 0, 'Permutation Importance')

	FIPS	State	County	Land Area
2081	39145	Ohio	Scioto	1585.9
485	13261	Georgia	Sumter	1257.0
1758	34037	New Jersey	Sussex	1350.0
1309	27061	Minnesota	Itasca	6903.1
1249	26107	Michigan	Mecosta	1439.5

	FIPS	State	County	Land Area
192	06071	California	San Bernardino	51960.0
69	04005	Arizona	Coconino	48223.6
1725	32023	Nevada	Nye	47001.4
1717	32007	Nevada	Elko	44500.2
75	04015	Arizona	Mohave	34479.2
67	04001	Arizona	Apache	29022.7
1722	32017	Nevada	Lincoln	27543.8
3106	56037	Wyoming	Sweetwater	27003.0
170	06027	California	Inyo	26397.5
2186	41025	Oregon	Harney	26249.4

	fips	state	name	pop2000	pop2010	pop2011	pop2012	pop2013	pop2014	pop2015	...	uninsured_65_and_older_2019	uninsured_under_19_2019	uninsured_under_6_2019	veterans_2019	white_2019	white_not_hispanic_2019	FIPS	State	County	Land Area
0	01001	Alabama	Autauga County	43671.0	54571	55199.0	54927.0	54695.0	54864.0	54838.0	...	0.0	1.7	1.7	12.6	76.8	74.6	01001	Alabama	Autauga	1543.7
1	01003	Alabama	Baldwin County	140415.0	182265	186534.0	190048.0	194736.0	199064.0	202863.0	...	0.3	3.8	2.2	11.8	86.2	83.1	01003	Alabama	Baldwin	4135.0
2	01005	Alabama	Barbour County	29038.0	27457	27351.0	27175.0	26947.0	26749.0	26264.0	...	0.3	3.3	3.4	6.6	46.8	45.8	01005	Alabama	Barbour	2292.1
3	01007	Alabama	Bibb County	20826.0	22915	22745.0	22658.0	22503.0	22533.0	22561.0	...	0.0	2.0	4.5	8.0	76.8	74.5	01007	Alabama	Bibb	1611.9
4	01009	Alabama	Blount County	51024.0	57322	57562.0	57595.0	57623.0	57546.0	57590.0	...	0.2	5.9	6.1	7.7	95.5	86.9	01009	Alabama	Blount	1672.3
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
3100	56037	Wyoming	Sweetwater County	37613.0	43806	44013.0	45042.0	45145.0	44981.0	44732.0	...	0.5	9.2	13.8	8.6	93.4	79.6	56037	Wyoming	Sweetwater	27003.0
3101	56039	Wyoming	Teton County	18251.0	21294	21476.0	21709.0	22326.0	22817.0	23029.0	...	0.0	10.1	5.9	5.3	89.3	81.3	56039	Wyoming	Teton	10380.6
3102	56041	Wyoming	Uinta County	19742.0	21118	20899.0	20999.0	20960.0	20845.0	20780.0	...	0.6	6.8	1.8	7.4	93.4	87.5	56041	Wyoming	Uinta	5391.7
3103	56043	Wyoming	Washakie County	8289.0	8533	8460.0	8421.0	8427.0	8288.0	8296.0	...	1.5	7.0	7.8	11.9	89.7	81.9	56043	Wyoming	Washakie	5802.0
3104	56045	Wyoming	Weston County	6644.0	7208	7141.0	7074.0	7136.0	7142.0	7181.0	...	0.0	8.6	7.1	10.3	97.4	96.4	56045	Wyoming	Weston	6210.6

	fips	state	name	pop2000	pop2010	pop2011	pop2012	pop2013	pop2014	pop2015	...	two_plus_races_2019	unemployment_rate_2019	uninsured_2019	uninsured_65_and_older_2019	uninsured_under_19_2019	uninsured_under_6_2019	veterans_2019	white_2019	white_not_hispanic_2019	Land Area
0	01001	Alabama	Autauga County	43671.0	54571	55199.0	54927.0	54695.0	54864.0	54838.0	...	2.2	3.5	7.1	0.0	1.7	1.7	12.6	76.8	74.6	1543.7
1	01003	Alabama	Baldwin County	140415.0	182265	186534.0	190048.0	194736.0	199064.0	202863.0	...	1.7	4.0	8.9	0.3	3.8	2.2	11.8	86.2	83.1	4135.0
2	01005	Alabama	Barbour County	29038.0	27457	27351.0	27175.0	26947.0	26749.0	26264.0	...	1.2	9.4	11.3	0.3	3.3	3.4	6.6	46.8	45.8	2292.1
3	01007	Alabama	Bibb County	20826.0	22915	22745.0	22658.0	22503.0	22533.0	22561.0	...	0.6	7.0	10.7	0.0	2.0	4.5	8.0	76.8	74.5	1611.9
4	01009	Alabama	Blount County	51024.0	57322	57562.0	57595.0	57623.0	57546.0	57590.0	...	1.6	3.1	10.8	0.2	5.9	6.1	7.7	95.5	86.9	1672.3
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
3100	56037	Wyoming	Sweetwater County	37613.0	43806	44013.0	45042.0	45145.0	44981.0	44732.0	...	2.3	5.7	11.3	0.5	9.2	13.8	8.6	93.4	79.6	27003.0
3101	56039	Wyoming	Teton County	18251.0	21294	21476.0	21709.0	22326.0	22817.0	23029.0	...	0.7	0.7	12.7	0.0	10.1	5.9	5.3	89.3	81.3	10380.6
3102	56041	Wyoming	Uinta County	19742.0	21118	20899.0	20999.0	20960.0	20845.0	20780.0	...	3.5	5.5	11.2	0.6	6.8	1.8	7.4	93.4	87.5	5391.7
3103	56043	Wyoming	Washakie County	8289.0	8533	8460.0	8421.0	8427.0	8288.0	8296.0	...	3.8	4.1	15.0	1.5	7.0	7.8	11.9	89.7	81.9	5802.0
3104	56045	Wyoming	Weston County	6644.0	7208	7141.0	7074.0	7136.0	7142.0	7181.0	...	1.3	4.0	11.8	0.0	8.6	7.1	10.3	97.4	96.4	6210.6

	year	state	office	district	candidate	party	candidatevotes	totalvotes
0	1976	ALABAMA	US HOUSE	1	BILL DAVENPORT	DEMOCRAT	58906	157170
1	1976	ALABAMA	US HOUSE	1	JACK EDWARDS	REPUBLICAN	98257	157170
2	1976	ALABAMA	US HOUSE	1	WRITEIN	NaN	7	157170
3	1976	ALABAMA	US HOUSE	2	J CAROLE KEAHEY	DEMOCRAT	66288	156362
4	1976	ALABAMA	US HOUSE	2	WILLIAM L "BILL" DICKINSON	REPUBLICAN	90069	156362
...	...	...	...	...	...	...	...	...
31098	2020	WYOMING	US HOUSE	0	LYNNETTE GREY BULL	DEMOCRAT	66576	278503
31099	2020	WYOMING	US HOUSE	0	OVERVOTES	NaN	1274	278503
31100	2020	WYOMING	US HOUSE	0	RICHARD BRUBAKER	LIBERTARIAN	10154	278503
31101	2020	WYOMING	US HOUSE	0	UNDERVOTES	NaN	6337	278503
31102	2020	WYOMING	US HOUSE	0	WRITEIN	NaN	525	278503

	fips	state	name	pop2000	pop2010	pop2011	pop2012	pop2013	pop2014	pop2015	...	poverty_under_18_2019	two_plus_races_2019	unemployment_rate_2019	uninsured_2019	uninsured_65_and_older_2019	uninsured_under_19_2019	uninsured_under_6_2019	veterans_2019	white_2019	white_not_hispanic_2019
2830	51021	Virginia	Bland County	6871.0	6824	6776.0	6695.0	6675.0	6587.0	6535.0	...	NaN	1.0	5.4	7.5	0.0	8.1	10.9	6.8	95.0	93.9
2087	39089	Ohio	Licking County	145491.0	166492	167149.0	167568.0	168401.0	169295.0	170425.0	...	15.1	2.9	3.9	6.4	0.1	4.9	3.5	9.5	91.4	90.1
25	1051	Alabama	Elmore County	65874.0	79303	80006.0	80220.0	80555.0	80562.0	80912.0	...	15.2	1.7	4.0	7.4	0.0	3.1	2.3	10.6	75.3	73.2
1122	22019	Louisiana	Calcasieu Parish	183577.0	192768	193813.0	194506.0	195506.0	196641.0	198542.0	...	25.3	2.4	4.9	9.2	0.1	2.3	1.8	7.9	70.0	67.4
108	4023	Arizona	Santa Cruz County	38381.0	47420	47615.0	47292.0	46845.0	46468.0	46191.0	...	31.5	1.3	7.2	10.2	2.7	4.3	3.8	5.1	85.7	14.9

	fips	State	County	Year	Male life expectancy (years)	Years behind international frontier (male)	Female life expectancy (years)	Years behind international frontier (female)	White male life expectancy (years)	White female life expectancy (years)	...	Closest ranked countries for male life expectancy (lower)	Closest ranked countries for female life expectancy (lower)	Rank (male)	Rank (female)	Male life expectancy change 1987 to 2007 (years)	Female life expectancy change 1987 to 2007 (years)	Male life expectancy change 1987 to 1997 (years)	Female life expectancy change 1987 to 1997 (years)	Male life expectancy change 1997 to 2007 (years)	Female life expectancy change 1997 to 2007 (years)
0	1001	ALABAMA	AUTAUGA	1987	69.2	32	77.4	12	70.3	78.5	...	Albania,Bahrain,Guam,Jamaica,Macedonia, the Fo...	Guadeloupe,Israel,Malta,Montenegro,Portugal	2684	2661	3.7	0.6	1.5	0.8	2.2	-0.2
1	1001	ALABAMA	AUTAUGA	1988	69.3	32	77.3	14	70.5	78.4	...	Albania,Jamaica,Macedonia, the Former Yugoslav...	Barbados,Cuba,Mayotte,Reunion,Slovenia	2646	2691	3.7	0.6	1.5	0.8	2.2	-0.2
2	1001	ALABAMA	AUTAUGA	1989	69.8	25	77.5	14	71.0	78.6	...	Albania,Bahrain,Guam,Macedonia, the Former Yug...	Barbados,Mayotte,Reunion,Singapore,Slovenia	2522	2637	3.7	0.6	1.5	0.8	2.2	-0.2
3	1001	ALABAMA	AUTAUGA	1990	69.7	27	77.7	14	71.0	78.7	...	Albania,French Guiana,Macedonia, the Former Yu...	Barbados,Mayotte,Netherlands Antilles,Reunion,...	2585	2590	3.7	0.6	1.5	0.8	2.2	-0.2
4	1001	ALABAMA	AUTAUGA	1991	70.0	25	77.7	15	71.2	78.8	...	French Guiana,Macedonia, the Former Yugoslav R...	Barbados,Kuwait,Netherlands Antilles,Singapore...	2534	2616	3.7	0.6	1.5	0.8	2.2	-0.2

	index	Null Count
10	Black male life expectancy (years)	50593
11	Black female life expectancy (years)	47725
12	Closest ranked countries for male life expecta...	96
13	Closest ranked countries for female life expec...	11

	year	state	state_po	state_fips	state_cen	state_ic	office	district	stage	runoff	special	candidate	party	writein	mode	candidatevotes	totalvotes	unofficial	version	fusion_ticket
29058	2018	NEW YORK	NY	36	21	13	US HOUSE	8	GEN	NaN	False	ERNEST C JOHNSON	CONSERVATIVE	False	TOTAL	9997	191567	False	20220331	False
30797	2020	SOUTH CAROLINA	SC	45	57	48	US HOUSE	2	GEN	False	False	ADAIR FORD BOROUGHS	DEMOCRAT	False	TOTAL	155118	364215	False	20220331	False
22920	2010	GEORGIA	GA	13	58	44	US HOUSE	3	GEN	NaN	False	WRITEIN	NaN	True	TOTAL	3	242239	False	20220331	False
20574	2006	NEW YORK	NY	36	21	13	US HOUSE	1	GEN	False	False	TIMOTHY H BISHOP	DEMOCRAT	False	TOTAL	92546	179113	False	20220331	True
23974	2010	VIRGINIA	VA	51	54	40	US HOUSE	7	GEN	NaN	False	WRITEIN	NaN	True	TOTAL	413	233402	False	20220331	False

	year	state	office	district	candidate	party	candidatevotes	totalvotes
29636	2020	ALABAMA	US HOUSE	1	JAMES AVERHART	DEMOCRAT	116949	329075
29637	2020	ALABAMA	US HOUSE	1	JERRY CARL	REPUBLICAN	211825	329075
29638	2020	ALABAMA	US HOUSE	1	WRITEIN	NaN	301	329075
29639	2020	ALABAMA	US HOUSE	2	BARRY MOORE	REPUBLICAN	197996	303569
29640	2020	ALABAMA	US HOUSE	2	PHYLLIS HARVEY-HALL	DEMOCRAT	105286	303569
...	...	...	...	...	...	...	...	...
31098	2020	WYOMING	US HOUSE	0	LYNNETTE GREY BULL	DEMOCRAT	66576	278503
31099	2020	WYOMING	US HOUSE	0	OVERVOTES	NaN	1274	278503
31100	2020	WYOMING	US HOUSE	0	RICHARD BRUBAKER	LIBERTARIAN	10154	278503
31101	2020	WYOMING	US HOUSE	0	UNDERVOTES	NaN	6337	278503
31102	2020	WYOMING	US HOUSE	0	WRITEIN	NaN	525	278503

	year	state	office	district	candidate	party	candidatevotes	totalvotes
29695	2020	CALIFORNIA	US HOUSE	1	AUDREY DENNEY	DEMOCRAT	154073	358263
29696	2020	CALIFORNIA	US HOUSE	1	DOUG LAMALFA	REPUBLICAN	204190	358263
29697	2020	CALIFORNIA	US HOUSE	2	DALE K MENSING	REPUBLICAN	94320	388755
29698	2020	CALIFORNIA	US HOUSE	2	JARED HUFFMAN	DEMOCRAT	294435	388755
29699	2020	CALIFORNIA	US HOUSE	3	JOHN GARAMENDI	DEMOCRAT	176043	321988
...	...	...	...	...	...	...	...	...
31098	2020	WYOMING	US HOUSE	0	LYNNETTE GREY BULL	DEMOCRAT	66576	278503
31099	2020	WYOMING	US HOUSE	0	OVERVOTES	NaN	1274	278503
31100	2020	WYOMING	US HOUSE	0	RICHARD BRUBAKER	LIBERTARIAN	10154	278503
31101	2020	WYOMING	US HOUSE	0	UNDERVOTES	NaN	6337	278503
31102	2020	WYOMING	US HOUSE	0	WRITEIN	NaN	525	278503

	candidate	candidatevotes
21	BARBARA LEE	327863
57	DUSTY JOHNSON	321984
44	DANIEL WEBSTER	316979
96	JOHN H RUTHERFORD	308497
141	NEAL P DUNN	305337

	pop2000	pop2010	pop2011	pop2012	pop2013	pop2014	pop2015	pop2016	pop2017	age_under_5_2010	...	Rank (male)	Rank (female)	Male life expectancy change 1987 to 2007 (years)	Female life expectancy change 1987 to 2007 (years)	Male life expectancy change 1987 to 1997 (years)	Female life expectancy change 1987 to 1997 (years)	Male life expectancy change 1997 to 2007 (years)	Female life expectancy change 1997 to 2007 (years)	state	name
0	-0.156799	-0.139325	-0.138658	-0.140431	-0.142048	-0.142561	-0.143710	-0.143615	-0.144345	0.304693	...	1.211598	1.187277	0.537747	-0.566236	-0.152898	0.179472	0.921856	-0.913288	Alabama	Autauga County
1	-0.156799	-0.139325	-0.138658	-0.140431	-0.142048	-0.142561	-0.143710	-0.143615	-0.144345	0.304693	...	1.169856	1.220208	0.537747	-0.566236	-0.152898	0.179472	0.921856	-0.913288	Alabama	Autauga County
2	-0.156799	-0.139325	-0.138658	-0.140431	-0.142048	-0.142561	-0.143710	-0.143615	-0.144345	0.304693	...	1.033645	1.160933	0.537747	-0.566236	-0.152898	0.179472	0.921856	-0.913288	Alabama	Autauga County
3	-0.156799	-0.139325	-0.138658	-0.140431	-0.142048	-0.142561	-0.143710	-0.143615	-0.144345	0.304693	...	1.102849	1.109341	0.537747	-0.566236	-0.152898	0.179472	0.921856	-0.913288	Alabama	Autauga County
4	-0.156799	-0.139325	-0.138658	-0.140431	-0.142048	-0.142561	-0.143710	-0.143615	-0.144345	0.304693	...	1.046827	1.137881	0.537747	-0.566236	-0.152898	0.179472	0.921856	-0.913288	Alabama	Autauga County
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
65200	-0.284010	-0.291482	-0.291347	-0.290933	-0.290245	-0.289907	-0.289546	-0.289684	-0.291078	-0.482025	...	-0.685468	-0.911514	0.361729	0.494409	0.598760	-0.926569	0.016454	1.354572	Wyoming	Weston County
65201	-0.284010	-0.291482	-0.291347	-0.290933	-0.290245	-0.289907	-0.289546	-0.289684	-0.291078	-0.482025	...	-0.880996	-0.846750	0.361729	0.494409	0.598760	-0.926569	0.016454	1.354572	Wyoming	Weston County
65202	-0.284010	-0.291482	-0.291347	-0.290933	-0.290245	-0.289907	-0.289546	-0.289684	-0.291078	-0.482025	...	-0.766755	-0.919198	0.361729	0.494409	0.598760	-0.926569	0.016454	1.354572	Wyoming	Weston County
65203	-0.284010	-0.291482	-0.291347	-0.290933	-0.290245	-0.289907	-0.289546	-0.289684	-0.291078	-0.482025	...	-0.810694	-1.115685	0.361729	0.494409	0.598760	-0.926569	0.016454	1.354572	Wyoming	Weston County
65204	-0.284010	-0.291482	-0.291347	-0.290933	-0.290245	-0.289907	-0.289546	-0.289684	-0.291078	-0.482025	...	-0.938117	-1.302293	0.361729	0.494409	0.598760	-0.926569	0.016454	1.354572	Wyoming	Weston County

	pop2000	age_under_5_2010	age_over_65_2010	female_2010	white_2010	native_2010	asian_2010	pac_isl_2010	pac_isl_2017	other_single_race_2017	...	uninsured_under_6_2019	Year	Male life expectancy (years)	Years behind international frontier (female)	Male life expectancy change 1987 to 2007 (years)	Female life expectancy change 1987 to 2007 (years)	Male life expectancy change 1987 to 1997 (years)	Female life expectancy change 1987 to 1997 (years)	state	name
0	-0.156799	0.304693	-0.956608	0.573592	-0.294014	-0.203794	-0.091087	0.091194	0.064482	-0.196320	...	-0.648982	-1.651446	-1.507547	-0.363663	0.537747	-0.566236	-0.152898	0.179472	Alabama	Autauga County
1	-0.156799	0.304693	-0.956608	0.573592	-0.294014	-0.203794	-0.091087	0.091194	0.064482	-0.196320	...	-0.648982	-1.486301	-1.463804	-0.109769	0.537747	-0.566236	-0.152898	0.179472	Alabama	Autauga County
2	-0.156799	0.304693	-0.956608	0.573592	-0.294014	-0.203794	-0.091087	0.091194	0.064482	-0.196320	...	-0.648982	-1.321157	-1.245086	-0.109769	0.537747	-0.566236	-0.152898	0.179472	Alabama	Autauga County
3	-0.156799	0.304693	-0.956608	0.573592	-0.294014	-0.203794	-0.091087	0.091194	0.064482	-0.196320	...	-0.648982	-1.156012	-1.288829	-0.109769	0.537747	-0.566236	-0.152898	0.179472	Alabama	Autauga County
4	-0.156799	0.304693	-0.956608	0.573592	-0.294014	-0.203794	-0.091087	0.091194	0.064482	-0.196320	...	-0.648982	-0.990867	-1.157598	0.017177	0.537747	-0.566236	-0.152898	0.179472	Alabama	Autauga County
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
65200	-0.284010	-0.482025	-0.012875	-1.232841	0.750277	-0.064154	-0.381053	0.091194	-0.160987	-0.533812	...	0.310728	0.990867	1.029582	-0.109769	0.361729	0.494409	0.598760	-0.926569	Wyoming	Weston County
65201	-0.284010	-0.482025	-0.012875	-1.232841	0.750277	-0.064154	-0.381053	0.091194	-0.160987	-0.533812	...	0.310728	1.156012	1.204556	0.017177	0.361729	0.494409	0.598760	-0.926569	Wyoming	Weston County
65202	-0.284010	-0.482025	-0.012875	-1.232841	0.750277	-0.064154	-0.381053	0.091194	-0.160987	-0.533812	...	0.310728	1.321157	1.204556	0.017177	0.361729	0.494409	0.598760	-0.926569	Wyoming	Weston County
65203	-0.284010	-0.482025	-0.012875	-1.232841	0.750277	-0.064154	-0.381053	0.091194	-0.160987	-0.533812	...	0.310728	1.486301	1.292043	-0.109769	0.361729	0.494409	0.598760	-0.926569	Wyoming	Weston County
65204	-0.284010	-0.482025	-0.012875	-1.232841	0.750277	-0.064154	-0.381053	0.091194	-0.160987	-0.533812	...	0.310728	1.651446	1.423274	-0.236716	0.361729	0.494409	0.598760	-0.926569	Wyoming	Weston County

	year	state	office	district	candidate	party	candidatevotes	totalvotes
22553	2010	ALABAMA	US HOUSE	1	DAVID WALTER	CONSTITUTION	26357	156281
22554	2010	ALABAMA	US HOUSE	1	JO BONNER	REPUBLICAN	129063	156281
22556	2010	ALABAMA	US HOUSE	2	BOBBY BRIGHT	DEMOCRAT	106865	219028
22557	2010	ALABAMA	US HOUSE	2	MARTHA ROBY	REPUBLICAN	111645	219028
22559	2010	ALABAMA	US HOUSE	3	MIKE ROGERS	REPUBLICAN	117736	198139
...	...	...	...	...	...	...	...	...
25473	2012	WYOMING	US HOUSE	0	CHRIS HENRICHSEN	DEMOCRAT	57573	250700
25474	2012	WYOMING	US HOUSE	0	CYNTHIA M LUMMIS	REPUBLICAN	166452	250700
25475	2012	WYOMING	US HOUSE	0	DANIEL CLYDE CUMMINGS	CONSTITUTION	4963	250700
25476	2012	WYOMING	US HOUSE	0	DON WILLS	COUNTRY PARTY	3775	250700
25478	2012	WYOMING	US HOUSE	0	RICHARD P BRUBAKER	LIBERTARIAN	8442	250700

	state	VAP2010	VAP2012
0	Alabama	3646981.0	3684787.0
1	Alaska	522751.0	535397.0
2	Arizona	4763130.0	4905918.0
3	Arkansas	2204313.0	2234791.0
4	California	27955194.0	28680763.0
5	Colorado	3803001.0	3941163.0
6	Connecticut	2756819.0	2789596.0
7	Delaware	692372.0	709891.0
8	Florida	14797963.0	15270158.0
9	Georgia	7196177.0	7394822.0
10	Hawaii	1056446.0	1084314.0
11	Idaho	1138528.0	1163512.0
12	Illinois	9702168.0	9783173.0
13	Indiana	4874896.0	4929657.0
14	Iowa	2318473.0	2343874.0
15	Kansas	2126260.0	2156209.0
16	Kentucky	3315647.0	3357281.0
17	Louisiana	3415094.0	3476883.0
18	Maine	1053779.0	1058243.0
19	Maryland	4419968.0	4523759.0
20	Massachusetts	5129171.0	5239351.0
21	Michigan	7539072.0	7578532.0
22	Minnesota	4019316.0	4085045.0
23	Mississippi	2211757.0	2232845.0
24	Missouri	4563510.0	4603905.0
25	Montana	765792.0	778298.0
26	Nebraska	1366743.0	1389460.0
27	Nevada	2036187.0	2084445.0
28	New Hampshire	1029354.0	1039460.0
29	New Jersey	6725870.0	6820431.0
30	New Mexico	1540481.0	1566254.0
31	New York	15052653.0	15297720.0
32	North Carolina	7253813.0	7449864.0
33	North Dakota	522655.0	543720.0
34	Ohio	8806302.0	8844420.0
35	Oklahoma	2821701.0	2873141.0
36	Oregon	2965220.0	3026055.0
37	Pennsylvania	9909668.0	9990642.0
38	Rhode Island	828361.0	832044.0
39	South Carolina	3545098.0	3629956.0

	year	state	totalVotes
0	2010	ALABAMA	1359759
1	2010	ALASKA	252990
2	2010	ARIZONA	1698135
3	2010	ARKANSAS	773866
4	2010	CALIFORNIA	9644560
5	2010	COLORADO	1763106
6	2010	CONNECTICUT	1138116
7	2010	DELAWARE	305636
8	2010	FLORIDA	5116018
9	2010	GEORGIA	2468489
10	2010	HAWAII	360121
11	2010	IDAHO	447144
12	2010	ILLINOIS	3696108
13	2010	INDIANA	1747640
14	2010	IOWA	1094452
15	2010	KANSAS	835529
16	2010	KENTUCKY	1354051
17	2010	LOUISIANA	1035948
18	2010	MAINE	564326
19	2010	MARYLAND	1823638
20	2010	MASSACHUSETTS	2219813
21	2010	MICHIGAN	3194857
22	2010	MINNESOTA	2089062
23	2010	MISSISSIPPI	788549
24	2010	MISSOURI	1919791
25	2010	MONTANA	360341
26	2010	NEBRASKA	465510
27	2010	NEVADA	702788
28	2010	NEW HAMPSHIRE	449787
29	2010	NEW JERSEY	2121584
30	2010	NEW MEXICO	596651
31	2010	NEW YORK	4484408
32	2010	NORTH CAROLINA	2662110
33	2010	NORTH DAKOTA	236344
34	2010	OHIO	3825014
35	2010	OKLAHOMA	792980
36	2010	OREGON	1427027
37	2010	PENNSYLVANIA	3956401
38	2010	RHODE ISLAND	335004
39	2010	SOUTH CAROLINA	1339410

	year	state	totalVotes
0	2010	ALABAMA	1359759
1	2010	ALASKA	252990
2	2010	ARIZONA	1698135
3	2010	ARKANSAS	773866
4	2010	CALIFORNIA	9644560
5	2010	COLORADO	1763106
6	2010	CONNECTICUT	1138116
7	2010	DELAWARE	305636
8	2010	FLORIDA	5116018
9	2010	GEORGIA	2468489
10	2010	HAWAII	360121
11	2010	IDAHO	447144
12	2010	ILLINOIS	3696108
13	2010	INDIANA	1747640
14	2010	IOWA	1094452
15	2010	KANSAS	835529
16	2010	KENTUCKY	1354051
17	2010	LOUISIANA	1035948
18	2010	MAINE	564326
19	2010	MARYLAND	1823638
20	2010	MASSACHUSETTS	2219813
21	2010	MICHIGAN	3194857
22	2010	MINNESOTA	2089062
23	2010	MISSISSIPPI	788549
24	2010	MISSOURI	1919791
25	2010	MONTANA	360341
26	2010	NEBRASKA	465510
27	2010	NEVADA	702788
28	2010	NEW HAMPSHIRE	449787
29	2010	NEW JERSEY	2121584
30	2010	NEW MEXICO	596651
31	2010	NEW YORK	4484408
32	2010	NORTH CAROLINA	2662110
33	2010	NORTH DAKOTA	236344
34	2010	OHIO	3825014
35	2010	OKLAHOMA	792980
36	2010	OREGON	1427027
37	2010	PENNSYLVANIA	3956401
38	2010	RHODE ISLAND	335004
39	2010	SOUTH CAROLINA	1339410

	year	state	totalVotes	voter_turnout
0	2010	ALABAMA	1359759	0.372845
1	2010	ALASKA	252990	0.483959
2	2010	ARIZONA	1698135	0.356517
3	2010	ARKANSAS	773866	0.351069
4	2010	CALIFORNIA	9644560	0.345001
5	2010	COLORADO	1763106	0.463609
6	2010	CONNECTICUT	1138116	0.412837
7	2010	DELAWARE	305636	0.441433
8	2010	FLORIDA	5116018	0.345724
9	2010	GEORGIA	2468489	0.343028
10	2010	HAWAII	360121	0.340880
11	2010	IDAHO	447144	0.392739
12	2010	ILLINOIS	3696108	0.380957
13	2010	INDIANA	1747640	0.358498
14	2010	IOWA	1094452	0.472057
15	2010	KANSAS	835529	0.392957
16	2010	KENTUCKY	1354051	0.408382
17	2010	LOUISIANA	1035948	0.303344
18	2010	MAINE	564326	0.535526
19	2010	MARYLAND	1823638	0.412591
20	2010	MASSACHUSETTS	2219813	0.432782
21	2010	MICHIGAN	3194857	0.423773
22	2010	MINNESOTA	2089062	0.519756
23	2010	MISSISSIPPI	788549	0.356526
24	2010	MISSOURI	1919791	0.420683
25	2010	MONTANA	360341	0.470547
26	2010	NEBRASKA	465510	0.340598
27	2010	NEVADA	702788	0.345149
28	2010	NEW HAMPSHIRE	449787	0.436960
29	2010	NEW JERSEY	2121584	0.315436
30	2010	NEW MEXICO	596651	0.387315
31	2010	NEW YORK	4484408	0.297915
32	2010	NORTH CAROLINA	2662110	0.366995
33	2010	NORTH DAKOTA	236344	0.452199
34	2010	OHIO	3825014	0.434350
35	2010	OKLAHOMA	792980	0.281029
36	2010	OREGON	1427027	0.481255
37	2010	PENNSYLVANIA	3956401	0.399247
38	2010	RHODE ISLAND	335004	0.404418
39	2010	SOUTH CAROLINA	1339410	0.377820

	year	state	totalVotes
50	2012	ALABAMA	1927122
51	2012	ALASKA	288840
52	2012	ARIZONA	2173259
53	2012	ARKANSAS	1038054
54	2012	CALIFORNIA	12204357
55	2012	COLORADO	2450488
56	2012	CONNECTICUT	1465487
57	2012	DELAWARE	388059
58	2012	FLORIDA	7512911
59	2012	GEORGIA	3552967
60	2012	HAWAII	422539
61	2012	IDAHO	634983
62	2012	ILLINOIS	5057772
63	2012	INDIANA	2553743
64	2012	IOWA	1535469
65	2012	KANSAS	1057739
66	2012	KENTUCKY	1737037
67	2012	LOUISIANA	1705617
68	2012	MAINE	693801
69	2012	MARYLAND	2579538
70	2012	MASSACHUSETTS	2879565
71	2012	MICHIGAN	4574615
72	2012	MINNESOTA	2807826
73	2012	MISSISSIPPI	1208175
74	2012	MISSOURI	2675885
75	2012	MONTANA	479740
76	2012	NEBRASKA	772515
77	2012	NEVADA	973742
78	2012	NEW HAMPSHIRE	682018
79	2012	NEW JERSEY	3281778
80	2012	NEW MEXICO	765458
81	2012	NEW YORK	6456343
82	2012	NORTH CAROLINA	4379666
83	2012	NORTH DAKOTA	315716
84	2012	OHIO	5140157
85	2012	OKLAHOMA	1325935
86	2012	OREGON	1705571
87	2012	PENNSYLVANIA	5556330
88	2012	RHODE ISLAND	427321
89	2012	SOUTH CAROLINA	1791578
90	2012	SOUTH DAKOTA	361429
91	2012	TENNESSEE	2283173
92	2012	TEXAS	7663983
93	2012	UTAH	998897
94	2012	VERMONT	289663
95	2012	VIRGINIA	3733561
96	2012	WASHINGTON	3006266
97	2012	WEST VIRGINIA	641354
98	2012	WISCONSIN	2862341
99	2012	WYOMING	241205

	year	state	candidatevotes
0	1976	ALABAMA	984154
1	1976	ALASKA	117916
2	1976	ARIZONA	729002
3	1976	ARKANSAS	336383
4	1976	CALIFORNIA	7442501
...	...	...	...
1146	2020	VIRGINIA	4310779
1147	2020	WASHINGTON	3885792
1148	2020	WEST VIRGINIA	761385
1149	2020	WISCONSIN	3235981
1150	2020	WYOMING	270367

	state	pop2000	pop2010	pop2012	pop2014	pop2016
0	Alabama	4447100.0	4779736	4813946.0	4840037.0	4860545.0
1	Alaska	626932.0	710231	725255.0	731131.0	735745.0
2	Arizona	5130632.0	6392017	6544211.0	6706435.0	6908642.0
3	Arkansas	2673400.0	2915918	2949208.0	2964800.0	2988231.0
4	California	33871648.0	37253956	38019006.0	38701278.0	39296476.0
5	Colorado	4301261.0	5029196	5186330.0	5342311.0	5530105.0
6	Connecticut	3405565.0	3574097	3597705.0	3600188.0	3587685.0
7	Delaware	783600.0	897934	916868.0	934805.0	952698.0
8	District of Columbia	572059.0	601723	635630.0	660797.0	684336.0
9	Florida	15982378.0	18801310	19341327.0	19897747.0	20656589.0
10	Georgia	8186453.0	9687653	9911171.0	10083850.0	10313620.0
11	Hawaii	1211537.0	1360301	1392772.0	1417710.0	1428683.0
12	Idaho	1293953.0	1567582	1594673.0	1630391.0	1680026.0
13	Illinois	12419293.0	12830632	12878494.0	12882438.0	12835726.0
14	Indiana	6080485.0	6483802	6535665.0	6593182.0	6634007.0
15	Iowa	2926324.0	3046355	3074386.0	3105563.0	3130869.0
16	Kansas	2688418.0	2853118	2885316.0	2899553.0	2907731.0
17	Kentucky	4041769.0	4339367	4383673.0	4410415.0	4436113.0
18	Louisiana	4468976.0	4533372	4602681.0	4648797.0	4686157.0
19	Maine	1274923.0	1328361	1328101.0	1328903.0	1330232.0
20	Maryland	5296486.0	5773552	5891680.0	5970245.0	6024752.0
21	Massachusetts	6349097.0	6547629	6659627.0	6757925.0	6823721.0
22	Michigan	9938444.0	9883640	9886610.0	9914675.0	9933445.0
23	Minnesota	4919479.0	5303925	5377695.0	5452649.0	5525050.0
24	Mississippi	2844658.0	2967297	2982963.0	2988578.0	2985415.0
25	Missouri	5595211.0	5988927	6023267.0	6058014.0	6091176.0
26	Montana	902195.0	989415	1003522.0	1019931.0	1038656.0
27	Nebraska	1711263.0	1826341	1854862.0	1880920.0	1907603.0
28	Nevada	1998257.0	2700551	2752410.0	2831730.0	2939254.0
29	New Hampshire	1235786.0	1316470	1320923.0	1328684.0	1335015.0
30	New Jersey	8414350.0	8791894	8882095.0	8943010.0	8978416.0
31	New Mexico	1819046.0	2059179	2083590.0	2083207.0	2085432.0
32	New York	18976457.0	19378102	19625409.0	19773580.0	19836286.0
33	North Carolina	8049313.0	9535483	9755299.0	9941160.0	10156689.0
34	North Dakota	642200.0	672591	701380.0	738658.0	755548.0
35	Ohio	11353140.0	11536504	11546969.0	11593741.0	11622554.0
36	Oklahoma	3450654.0	3751351	3815298.0	3875008.0	3921207.0
37	Oregon	3421399.0	3831074	3893920.0	3960673.0	4085989.0
38	Pennsylvania	12281054.0	12702379	12768034.0	12790341.0	12787085.0
39	Rhode Island	1048319.0	1052567	1052761.0	1054782.0	1057566.0
40	South Carolina	4012012.0	4625364	4719009.0	4824758.0	4959822.0
41	South Dakota	754844.0	814180	832576.0	849455.0	861542.0
42	Tennessee	5689283.0	6346105	6450632.0	6540007.0	6649404.0
43	Texas	20851820.0	25145561	26078327.0	26954436.0	27904862.0
44	Utah	2233169.0	2763885	2854222.0	2938671.0	3044321.0
45	Vermont	608827.0	625741	625606.0	625665.0	623354.0
46	Virginia	7067927.0	7994802	8188656.0	8316902.0	8414380.0
47	Washington	5894121.0	6724540	6890899.0	7046931.0	7280934.0
48	West Virginia	1808344.0	1852994	1855360.0	1847624.0	1828637.0
49	Wisconsin	5363675.0	5686986	5721075.0	5751272.0	5772917.0
50	Wyoming	493782.0	563626	576608.0	583334.0	584910.0

	year	state	totalVotes	population
600	2000	ALABAMA	1431797	4447100
601	2000	ALASKA	273561	626932
602	2000	ARIZONA	1465656	5130632
603	2000	ARKANSAS	632513	2673400
604	2000	CALIFORNIA	10436607	33871648
605	2000	COLORADO	1619335	4301261
606	2000	CONNECTICUT	1313483	3405565
607	2000	DELAWARE	313126	783600
608	2000	FLORIDA	5009584	15982378
609	2000	GEORGIA	2416422	8186453
610	2000	HAWAII	340424	1211537
611	2000	IDAHO	492835	1293953
612	2000	ILLINOIS	4393184	12419293
613	2000	INDIANA	2156456	6080485
614	2000	IOWA	1275449	2926324
615	2000	KANSAS	1035961	2688418
616	2000	KENTUCKY	1435409	4041769
617	2000	LOUISIANA	1202172	4468976
618	2000	MAINE	638399	1274923
619	2000	MARYLAND	1924669	5296486
620	2000	MASSACHUSETTS	2334148	6349097
621	2000	MICHIGAN	4069625	9938444
622	2000	MINNESOTA	2363738	4919479
623	2000	MISSISSIPPI	986139	2844658
624	2000	MISSOURI	2325788	5595211
625	2000	MONTANA	410521	902195
626	2000	NEBRASKA	682496	1711263
627	2000	NEVADA	585204	1998257
628	2000	NEW HAMPSHIRE	556049	1235786
629	2000	NEW JERSEY	2988233	8414350
630	2000	NEW MEXICO	587514	1819046
631	2000	NEW YORK	5813877	18976457
632	2000	NORTH CAROLINA	2779168	8049313
633	2000	NORTH DAKOTA	285636	642200
634	2000	OHIO	4585038	11353140
635	2000	OKLAHOMA	1087515	3450654
636	2000	OREGON	1437425	3421399
637	2000	PENNSYLVANIA	4552010	12281054
638	2000	RHODE ISLAND	383862	1048319
639	2000	SOUTH CAROLINA	1320594	4012012
640	2000	SOUTH DAKOTA	314761	754844
641	2000	TENNESSEE	1852935	5689283
642	2000	TEXAS	5982441	20851820
643	2000	UTAH	758754	2233169
644	2000	VERMONT	282606	608827
645	2000	VIRGINIA	2412724	7067927
646	2000	WASHINGTON	2382411	5894121
647	2000	WEST VIRGINIA	579872	1808344
648	2000	WISCONSIN	2503018	5363675
649	2000	WYOMING	212312	493782

	year	state	totalVotes	population
650	2002	ALABAMA	1266562	4513806
651	2002	ALASKA	227274	636335
652	2002	ARIZONA	1194365	5207591
653	2002	ARKANSAS	675825	2713500
654	2002	CALIFORNIA	7256415	34379722
...	...	...	...	...
845	2008	VIRGINIA	3476710	7501640
846	2008	WASHINGTON	2914463	6255805
847	2008	WEST VIRGINIA	645414	1919310
848	2008	WISCONSIN	2770362	5692809
849	2008	WYOMING	249032	524082

	year	state	totalVotes
1100	2020	ALABAMA	2024821
1101	2020	ALASKA	351982
1102	2020	ARIZONA	3268249
1103	2020	ARKANSAS	1179396
1104	2020	CALIFORNIA	16724901
1105	2020	COLORADO	3164950
1106	2020	CONNECTICUT	1772927
1107	2020	DELAWARE	488270
1109	2020	FLORIDA	10464790
1110	2020	GEORGIA	4883611
1111	2020	HAWAII	526535
1112	2020	IDAHO	849909
1113	2020	ILLINOIS	5876819
1114	2020	INDIANA	2996444
1115	2020	IOWA	1637050
1116	2020	KANSAS	1358953
1117	2020	KENTUCKY	2115895
1118	2020	LOUISIANA	2147135
1119	2020	MAINE	809214
1120	2020	MARYLAND	2947893
1121	2020	MASSACHUSETTS	3304030
1122	2020	MICHIGAN	5423140
1123	2020	MINNESOTA	3189307
1124	2020	MISSISSIPPI	1227846
1125	2020	MISSOURI	2973421
1126	2020	MONTANA	601509
1127	2020	NEBRASKA	941298
1128	2020	NEVADA	1355607
1129	2020	NEW HAMPSHIRE	786806
1130	2020	NEW JERSEY	4432923
1131	2020	NEW MEXICO	903684
1132	2020	NEW YORK	8216413
1133	2020	NORTH CAROLINA	5324819
1134	2020	NORTH DAKOTA	355223
1135	2020	OHIO	5761540
1136	2020	OKLAHOMA	1551383
1137	2020	OREGON	2304718
1138	2020	PENNSYLVANIA	6779307
1139	2020	RHODE ISLAND	486287
1140	2020	SOUTH CAROLINA	2503382
1141	2020	SOUTH DAKOTA	397732
1142	2020	TENNESSEE	2841744
1143	2020	TEXAS	11093626
1144	2020	UTAH	1432232
1145	2020	VERMONT	354295
1146	2020	VIRGINIA	4310779
1147	2020	WASHINGTON	3885792
1148	2020	WEST VIRGINIA	761385
1149	2020	WISCONSIN	3235981
1150	2020	WYOMING	270367

	index	state	female_num
0	0	Alabama	2459237.0
1	1	Alaska	340578.0
2	2	Arizona	3215163.0
3	3	Arkansas	1484149.0
4	4	California	18730716.0
5	5	Colorado	2509043.0
6	6	Connecticut	1835148.0
7	7	Delaware	463031.0
8	9	Florida	9613839.0
9	10	Georgia	4957744.0
10	11	Hawaii	678539.0
11	12	Idaho	782011.0
12	13	Illinois	6538181.0
13	14	Indiana	3294306.0
14	15	Iowa	1538196.0
15	16	Kansas	1437803.0
16	17	Kentucky	2204181.0
17	18	Louisiana	2313889.0
18	19	Maine	678503.0
19	20	Maryland	2980796.0
20	21	Massachusetts	3381707.0
21	22	Michigan	5036146.0
22	23	Minnesota	2671946.0
23	24	Mississippi	1526108.0
24	25	Missouri	3055921.0
25	26	Montana	492719.0
26	27	Nebraska	919746.0
27	28	Nevada	1337536.0
28	29	New Hampshire	666927.0
29	30	New Jersey	4512482.0
30	31	New Mexico	1041638.0
31	32	New York	10001353.0
32	33	North Carolina	4889810.0
33	34	North Dakota	332680.0
34	35	Ohio	5904814.0
35	36	Oklahoma	1894345.0
36	37	Oregon	1934471.0
37	38	Pennsylvania	6511648.0
38	39	Rhode Island	543862.0
39	40	South Carolina	2375144.0
40	41	South Dakota	406841.0
41	42	Tennessee	3252774.0
42	43	Texas	12675500.0
43	44	Utah	1375645.0
44	45	Vermont	317597.0
45	46	Virginia	4071357.0
46	47	Washington	3375652.0
47	48	West Virginia	939524.0
48	49	Wisconsin	2864033.0
49	50	Wyoming	276192.0

	index	state	female_num	female_percent
0	0	Alabama	2459237.0	0.514513
1	1	Alaska	340578.0	0.479531
2	2	Arizona	3215163.0	0.502997
3	3	Arkansas	1484149.0	0.508982
4	4	California	18730716.0	0.502785
5	5	Colorado	2509043.0	0.498895
6	6	Connecticut	1835148.0	0.513458
7	7	Delaware	463031.0	0.515663
8	9	Florida	9613839.0	0.511339
9	10	Georgia	4957744.0	0.511759
10	11	Hawaii	678539.0	0.498815
11	12	Idaho	782011.0	0.498864
12	13	Illinois	6538181.0	0.509576
13	14	Indiana	3294306.0	0.508082
14	15	Iowa	1538196.0	0.504930
15	16	Kansas	1437803.0	0.503941
16	17	Kentucky	2204181.0	0.507950
17	18	Louisiana	2313889.0	0.510412
18	19	Maine	678503.0	0.510782
19	20	Maryland	2980796.0	0.516285
20	21	Massachusetts	3381707.0	0.516478
21	22	Michigan	5036146.0	0.509544
22	23	Minnesota	2671946.0	0.503768
23	24	Mississippi	1526108.0	0.514309
24	25	Missouri	3055921.0	0.510262
25	26	Montana	492719.0	0.497990
26	27	Nebraska	919746.0	0.503600
27	28	Nevada	1337536.0	0.495283
28	29	New Hampshire	666927.0	0.506603
29	30	New Jersey	4512482.0	0.513255
30	31	New Mexico	1041638.0	0.505851
31	32	New York	10001353.0	0.516116
32	33	North Carolina	4889810.0	0.512802
33	34	North Dakota	332680.0	0.494625
34	35	Ohio	5904814.0	0.511837
35	36	Oklahoma	1894345.0	0.504977
36	37	Oregon	1934471.0	0.504942
37	38	Pennsylvania	6511648.0	0.512632
38	39	Rhode Island	543862.0	0.516701
39	40	South Carolina	2375144.0	0.513504
40	41	South Dakota	406841.0	0.499694
41	42	Tennessee	3252774.0	0.512562
42	43	Texas	12675500.0	0.504085
43	44	Utah	1375645.0	0.497722
44	45	Vermont	317597.0	0.507553
45	46	Virginia	4071357.0	0.509251
46	47	Washington	3375652.0	0.501990
47	48	West Virginia	939524.0	0.507030
48	49	Wisconsin	2864033.0	0.503612
49	50	Wyoming	276192.0	0.490027

	index	state	bachelors_sum_2016	bachelors_sum_2017	pop2017	pop2016
0	0	Alabama	1177586.0	1206184.0	4874747.0	4860545.0
1	1	Alaska	209941.0	210800.0	733972.0	735745.0
2	2	Arizona	1940984.0	2001339.0	7016270.0	6908642.0
3	3	Arkansas	651955.0	671790.0	3004279.0	2988231.0
4	4	California	12423475.0	12706851.0	39536653.0	39296476.0
5	5	Colorado	2140208.0	2209034.0	5607154.0	5530105.0
6	6	Connecticut	1368712.0	1381762.0	3588184.0	3587685.0
7	7	Delaware	290449.0	297954.0	961939.0	952698.0
8	9	Florida	5779295.0	5987483.0	20984400.0	20656589.0
9	10	Georgia	3049269.0	3136717.0	10429379.0	10313620.0
10	11	Hawaii	448897.0	457543.0	1427538.0	1428683.0
11	12	Idaho	442734.0	460636.0	1716943.0	1680026.0
12	13	Illinois	4247707.0	4302283.0	12802023.0	12835726.0
13	14	Indiana	1656191.0	1711925.0	6666818.0	6634007.0
14	15	Iowa	869795.0	889392.0	3145711.0	3130869.0
15	16	Kansas	925703.0	948527.0	2913123.0	2907731.0
16	17	Kentucky	1018507.0	1045038.0	4454189.0	4436113.0
17	18	Louisiana	1079751.0	1099420.0	4684333.0	4686157.0
18	19	Maine	391004.0	405369.0	1335907.0	1330232.0
19	20	Maryland	2317761.0	2364163.0	6052177.0	6024752.0
20	21	Massachusetts	2810023.0	2884571.0	6859819.0	6823721.0
21	22	Michigan	2746139.0	2819725.0	9962311.0	9933445.0
22	23	Minnesota	1898587.0	1943724.0	5576606.0	5525050.0
23	24	Mississippi	635003.0	644336.0	2984100.0	2985415.0
24	25	Missouri	1691739.0	1730133.0	6113532.0	6091176.0
25	26	Montana	313942.0	326078.0	1050493.0	1038656.0
26	27	Nebraska	578814.0	593679.0	1920076.0	1907603.0
27	28	Nevada	681648.0	711241.0	2998039.0	2939254.0
28	29	New Hampshire	474475.0	484683.0	1342795.0	1335015.0
29	30	New Jersey	3358033.0	3424183.0	9005644.0	8978416.0
30	31	New Mexico	549578.0	555094.0	2088070.0	2085432.0
31	32	New York	6842027.0	6957052.0	19849399.0	19836286.0
32	33	North Carolina	2986382.0	3104071.0	10273419.0	10156689.0
33	34	North Dakota	215489.0	220887.0	755393.0	755548.0
34	35	Ohio	3122615.0	3198644.0	11658609.0	11622554.0
35	36	Oklahoma	969557.0	985379.0	3930864.0	3921207.0
36	37	Oregon	1288213.0	1338134.0	4142776.0	4085989.0
37	38	Pennsylvania	3764539.0	3863560.0	12805537.0	12787085.0
38	39	Rhode Island	342406.0	348216.0	1059639.0	1057566.0
39	40	South Carolina	1319135.0	1363079.0	5024369.0	4959822.0
40	41	South Dakota	236484.0	242101.0	869666.0	861542.0
41	42	Tennessee	1709642.0	1772301.0	6715984.0	6649404.0
42	43	Texas	7865606.0	8139329.0	28304596.0	27904862.0
43	44	Utah	972715.0	1018490.0	3101833.0	3044321.0
44	45	Vermont	227717.0	231512.0	623657.0	623354.0
45	46	Virginia	3128649.0	3204193.0	8470020.0	8414380.0
46	47	Washington	2429313.0	2532132.0	7405743.0	7280934.0
47	48	West Virginia	363285.0	366421.0	1815857.0	1828637.0
48	49	Wisconsin	1650746.0	1694242.0	5795483.0	5772917.0
49	50	Wyoming	152941.0	156015.0	579315.0	584910.0

	year	state	totalVotes	population	VAP	can_vote_percent	voter_turnout	female_percent	bachelors_percent	predicted_turnout	abs_diff
893	2010	UTAH	640495	2763885	1893012	0.684910	0.338347	0.497722	0.314749	0.504164	0.165817
860	2010	HAWAII	360121	1360301	1056446	0.776627	0.340880	0.498815	0.309514	0.479731	0.138852
859	2010	GEORGIA	2468489	9687653	7196177	0.742819	0.343028	0.511759	0.291242	0.481436	0.138408
882	2010	NORTH CAROLINA	2662110	9535483	7253813	0.760718	0.366995	0.512802	0.289643	0.489233	0.122238
892	2010	TEXAS	4744189	25145561	18283803	0.727119	0.259475	0.504085	0.277665	0.379802	0.120327
854	2010	CALIFORNIA	9644560	37253956	27955194	0.750395	0.345001	0.502785	0.311429	0.459730	0.114729
865	2010	KANSAS	835529	2853118	2126260	0.745241	0.392957	0.503941	0.313608	0.502215	0.109258
879	2010	NEW JERSEY	2121584	8791894	6725870	0.765008	0.315436	0.513255	0.368430	0.423926	0.108490
862	2010	ILLINOIS	3696108	12830632	9702168	0.756172	0.380957	0.509576	0.325989	0.482796	0.101839
881	2010	NEW YORK	4484408	19378102	15052653	0.776787	0.297915	0.516116	0.339777	0.388761	0.090846
861	2010	IDAHO	447144	1567582	1138528	0.726296	0.392739	0.498864	0.259595	0.483192	0.090454
876	2010	NEBRASKA	465510	1826341	1366743	0.748350	0.340598	0.503600	0.298896	0.426366	0.085767
880	2010	NEW MEXICO	596651	2059179	1540481	0.748104	0.387315	0.505851	0.259599	0.469367	0.082052
895	2010	VIRGINIA	2184271	7994802	6142207	0.768275	0.355617	0.509251	0.366272	0.437205	0.081588
852	2010	ARIZONA	1698135	6392017	4763130	0.745169	0.356517	0.502997	0.276757	0.437205	0.080688
871	2010	MICHIGAN	3194857	9883640	7539072	0.762783	0.423773	0.509544	0.272328	0.502215	0.078442
889	2010	SOUTH CAROLINA	1339410	4625364	3545098	0.766447	0.377820	0.513504	0.261995	0.456187	0.078366
888	2010	RHODE ISLAND	335004	1052567	828361	0.786991	0.404418	0.516701	0.318936	0.481418	0.077000
887	2010	PENNSYLVANIA	3956401	12702379	9909668	0.780143	0.399247	0.512632	0.290008	0.475896	0.076649
877	2010	NEVADA	702788	2700551	2036187	0.753989	0.345149	0.495283	0.228451	0.418019	0.072870
891	2010	TENNESSEE	1559120	6346105	4849154	0.764115	0.321524	0.512562	0.253275	0.388206	0.066682
857	2010	DELAWARE	305636	897934	692372	0.771072	0.441433	0.515663	0.300320	0.504745	0.063312
869	2010	MARYLAND	1823638	5773552	4419968	0.765554	0.412591	0.516285	0.378965	0.471649	0.059058
850	2010	ALABAMA	1359759	4779736	3646981	0.763009	0.372845	0.514513	0.238659	0.425759	0.052914
856	2010	CONNECTICUT	1138116	3574097	2756819	0.771333	0.412837	0.513458	0.375809	0.465203	0.052366
878	2010	NEW HAMPSHIRE	449787	1316470	1029354	0.781905	0.436960	0.506603	0.350103	0.488089	0.051129
885	2010	OKLAHOMA	792980	3751351	2821701	0.752183	0.281029	0.504977	0.243569	0.328256	0.047227
884	2010	OHIO	3825014	11536504	8806302	0.763342	0.434350	0.511837	0.264659	0.481436	0.047086
858	2010	FLORIDA	5116018	18801310	14797963	0.787071	0.345724	0.511339	0.275604	0.392804	0.047080
853	2010	ARKANSAS	773866	2915918	2204313	0.755959	0.351069	0.508982	0.214918	0.391468	0.040399
873	2010	MISSISSIPPI	788549	2967297	2211757	0.745378	0.356526	0.514309	0.209527	0.395209	0.038683
868	2010	MAINE	564326	1328361	1053779	0.793293	0.535526	0.510782	0.289550	0.498661	0.036865
866	2010	KENTUCKY	1354051	4339367	3315647	0.764085	0.408382	0.507950	0.226168	0.443323	0.034941
875	2010	MONTANA	360341	989415	765792	0.773985	0.470547	0.497990	0.297747	0.504745	0.034199
863	2010	INDIANA	1747640	6483802	4874896	0.751858	0.358498	0.508082	0.245926	0.392370	0.033872
896	2010	WASHINGTON	2479409	6724540	5143903	0.764945	0.482009	0.501990	0.328674	0.513525	0.031516
855	2010	COLORADO	1763106	5029196	3803001	0.756185	0.463609	0.498895	0.381234	0.494222	0.030613
867	2010	LOUISIANA	1035948	4533372	3415094	0.753323	0.303344	0.510412	0.226974	0.324341	0.020997
899	2010	WYOMING	186682	563626	428209	0.759740	0.435960	0.490027	0.257575	0.455831	0.019871
872	2010	MINNESOTA	2089062	5303925	4019316	0.757800	0.519756	0.503768	0.338504	0.502215	0.017540
897	2010	WEST VIRGINIA	514373	1852994	1465505	0.790885	0.350987	0.507030	0.195699	0.334404	0.016583
883	2010	NORTH DAKOTA	236344	672591	522655	0.777077	0.452199	0.494625	0.280952	0.464119	0.011920
874	2010	MISSOURI	1919791	5988927	4563510	0.761991	0.420683	0.510262	0.273591	0.431710	0.011027
890	2010	SOUTH DAKOTA	319426	814180	611357	0.750887	0.522487	0.499694	0.270392	0.512252	0.010235
870	2010	MASSACHUSETTS	2219813	6547629	5129171	0.783363	0.432782	0.516478	0.405656	0.425645	0.007137
864	2010	IOWA	1094452	3046355	2318473	0.761065	0.472057	0.504930	0.273666	0.478054	0.005997
894	2010	VERMONT	238335	625741	496428	0.793344	0.480100	0.507553	0.359857	0.474959	0.005141
851	2010	ALASKA	252990	710231	522751	0.736030	0.483959	0.479531	0.281086	0.488368	0.004409
886	2010	OREGON	1427027	3831074	2965220	0.773992	0.481255	0.504942	0.310570	0.485345	0.004090
898	2010	WISCONSIN	2138775	5686986	4348246	0.764596	0.491871	0.503612	0.281679	0.493941	0.002071

Machine Learning - HW3¶

Table of Contents¶

1 Libraries¶

2 Data Exploration and Visualization¶

3 Data Pre-Processing¶

4 Dimensionality Reduction¶

5 Regression¶

6 Classification¶

1 - Libraries ¶

2 Data Exploration and Visualization ¶

US Land Area Data¶

County Complete Data¶

Merged County Complete data with US Landarea¶

Life Expectancy Data¶

Merging the already merged County Complete and US Landarea with Life Expectancy dataframe¶

1976-2020 House Elections Data¶

3 Data Pre-Processing ¶

Merged Data¶

House Elections Data¶

Imputation¶

Normalization¶

Back to Imputation¶

Feature Selection: Filter Method¶

4 - Dimensionality Reduction ¶

4.1 PCA¶

Most & Least Effective Features to Separate the four States¶

Most Effective features¶

Least Effective features¶

Measuring the goodness of separation - Bonus¶

Most Effective Features¶

Least Effective Features¶

2. Isomap¶

Logistic Regression Feature Importance¶

Most Effective Features¶

Least Effective Features¶

Measuring the goodness of separation¶

Most Effective Features¶

Least Effective Features¶

5 - Regression ¶

Calculate VAP (Voting Age Population)¶

C2¶

Models & Predictions¶

1. Adaboost Regressor¶

2010¶

Rank the states by how well your model predicted turnout.¶

2012¶

Rank the states by how well your model predicted turnout.¶

Feature Importance¶

2. Regression Tree¶

2010¶

Rank the states by how well your model predicted turnout.¶

2012¶

Rank the states by how well your model predicted turnout.¶

Feature Importance¶

3. Random Forest Regressor¶

2010¶

Rank the states by how well your model predicted turnout.¶

2012¶

Rank the states by how well your model predicted turnout.¶

Feature Importance¶

Comparing The Results - 2010¶

Comparing The Results - 2012¶

6 - Classification ¶

Models and Predictions¶

1. Random Forest Classifier¶

2010¶

2012¶

2. Gradient Boosting Classifer¶

2010¶

2012¶

3. SVM¶

Call the SVC() model from sklearn and fit the model to the training data¶

2010¶

2012¶

Comparing The Results¶

2010¶

2012¶

1 Libraries ¶

2 Data Exploration and Visualization ¶

3 Data Pre-Processing ¶

4 Dimensionality Reduction ¶

5 Regression ¶

6 Classification ¶

	Importance
totalVotes	0.386547
population	0.195172
VAP	0.193100
bachelors_percent	0.134423
can_vote_percent	0.070400
female_percent	0.020358

	Importance
totalVotes	0.363255
bachelors_percent	0.214220
VAP	0.191044
population	0.113330
can_vote_percent	0.095493
female_percent	0.022659

	Importance
totalVotes	0.366492
VAP	0.182014
bachelors_percent	0.172571
population	0.131256
can_vote_percent	0.121287
female_percent	0.026379

Model	MSE	MAE	R2
AdaBoostRegressor	0.005	0.058	-0.194
DecisionTreeRegressor	0.006	0.06	-0.346
RandomForestRegressor	0.004	0.051	0.042

	index	year	state	candidatevotes	state_class
0	0	1976	ALABAMA	667052	D
1	5	1976	ALASKA	83722	R
2	6	1976	ARIZONA	355747	D
3	10	1976	ARKANSAS	260998	D
4	13	1976	CALIFORNIA	4144324	D
...	...	...	...	...	...
1145	4720	2020	VIRGINIA	2253974	D
1146	4723	2020	WASHINGTON	2340356	D
1147	4726	2020	WEST VIRGINIA	514268	R
1148	4730	2020	WISCONSIN	1661399	R
1149	4734	2020	WYOMING	185732	R

	candidatevotes	VAP	can_vote_percent	female_percent	bachelors_percent	voter_turnout
0	-0.294685	-0.330540	-0.836712	1.061433	-1.323680	-0.573026
1	-0.853522	-0.822472	-0.554075	-3.628490	-0.500820	-1.035532
2	-0.592919	-0.154795	1.390405	-0.482556	-0.584780	-1.792873
3	-0.683690	-0.557699	-0.745093	0.319851	-1.784116	-1.797560
4	3.036587	3.496959	-0.728810	-0.510980	0.087663	-0.610882
...	...	...	...	...	...	...
1145	1.225607	-0.789922	1.783793	0.128364	1.840198	1.681021
1146	1.308362	0.547823	0.553965	0.355885	1.979117	1.488724
1147	-0.441054	0.313121	-0.012943	-0.617509	1.164949	1.855327
1148	0.657912	-0.566352	1.869442	0.058220	-1.714563	0.308250
1149	-0.755796	0.106264	0.818607	-0.400088	0.147285	1.871884

	candidatevotes	VAP	can_vote_percent	female_percent	bachelors_percent	voter_turnout
850	-0.057883	-0.202280	0.093931	1.071788	-1.111750	-0.384557
851	-0.847113	-0.813405	-1.333973	-3.644393	-0.229386	1.318053
852	-0.072764	0.016048	-0.850284	-0.480846	-0.319417	-0.634760
853	-0.569423	-0.484478	-0.279216	0.326054	-1.605482	-0.718234
854	4.463940	4.552604	-0.573652	-0.509429	0.401653	-0.811221
855	-0.090360	-0.171761	-0.267245	-1.033759	1.853397	1.006231
856	-0.356354	-0.376403	0.534492	0.929509	1.740566	0.228239
857	-0.849079	-0.780226	0.520692	1.226758	0.170619	0.666427
858	2.174174	1.978943	1.367431	0.643827	-0.343395	-0.800129
859	0.597473	0.491971	-0.974612	0.700485	-0.018173	-0.841450
860	-0.792602	-0.709010	0.814662	-1.044559	0.361828	-0.874366
861	-0.752803	-0.692954	-1.849147	-1.037932	-0.676336	-0.079725
862	0.969282	0.982164	-0.267903	0.406160	0.704469	-0.260258
863	0.004296	0.037910	-0.496258	0.204814	-0.960616	-0.604401
864	-0.396435	-0.462147	-0.008971	-0.220194	-0.383694	1.135683
865	-0.470415	-0.499746	-0.846458	-0.353534	0.446968	-0.076378
866	-0.132716	-0.267092	0.150907	0.186943	-1.371520	0.159981
867	-0.313169	-0.247639	-0.418695	0.518925	-1.354754	-1.449532
868	-0.696785	-0.709532	1.696724	0.568774	-0.053367	2.108221
869	0.144599	-0.051078	0.228653	1.310612	1.806197	0.224471
870	0.392009	0.087648	1.171195	1.336700	2.361296	0.533864
871	0.750784	0.559044	0.081973	0.401811	-0.411531	0.395821
872	0.002235	-0.129448	-0.181737	-0.376894	0.964732	1.866570
873	-0.582070	-0.483022	-0.839214	1.044286	-1.717595	-0.634615
874	0.143781	-0.023000	0.040072	0.498638	-0.385264	0.348469
875	-0.801929	-0.765864	0.674831	-1.155799	0.117107	1.112538
876	-0.684152	-0.648313	-0.681882	-0.399451	0.141012	-0.878682
877	-0.652774	-0.517365	-0.383429	-1.520832	-1.324044	-0.808947
878	-0.788506	-0.714309	1.094005	0.005291	1.205971	0.597891
879	0.092533	0.399976	0.199741	0.902143	1.587099	-1.264238
880	-0.705744	-0.614329	-0.694898	-0.096009	-0.676256	-0.162837
881	1.743053	2.028762	0.823136	1.287913	0.991206	-1.532723
882	0.504323	0.503245	-0.027319	0.841028	-0.051434	-0.474205
883	-0.895789	-0.813424	0.838498	-1.609557	-0.232170	0.831390
884	1.158040	0.806925	0.111580	0.711047	-0.571025	0.557884
885	-0.479570	-0.363712	-0.479058	-0.213891	-1.009617	-1.791465
886	-0.251251	-0.335638	0.675213	-0.218545	0.383797	1.276621
887	1.137825	1.022752	1.000752	0.818197	-0.043841	0.019996
888	-0.836085	-0.753625	1.363217	1.366696	0.557775	0.099237
889	-0.229292	-0.222209	0.275915	0.935769	-0.626429	-0.308321
890	-0.870266	-0.796073	-0.547641	-0.926077	-0.451779	1.908422
891	-0.014491	0.032875	0.152473	0.808775	-0.807778	-1.170954
892	2.231398	2.660801	-1.805596	-0.334114	-0.300526	-2.121741
893	-0.616893	-0.545371	-4.039526	-1.192027	0.470705	-0.913175
894	-0.869942	-0.818554	1.699453	0.133495	1.408815	1.258920
895	0.232211	0.285806	0.372648	0.362290	1.542232	-0.648551
896	0.350109	0.090530	0.196400	-0.616554	0.760306	1.288179
897	-0.732101	-0.628995	1.569293	0.062958	-2.005173	-0.719493
898	0.210493	-0.065107	0.177924	-0.397916	-0.217057	1.439287
899	-0.893804	-0.831898	-0.079098	-2.229375	-0.718339	0.582560

	candidatevotes	VAP	can_vote_percent	female_percent	bachelors_percent	voter_turnout
900	-0.095371	-0.208206	0.086671	1.071788	-1.111750	-0.167539
901	-0.883483	-0.808188	-1.354648	-3.644393	-0.229386	0.070422
902	-0.172023	0.024428	-0.748984	-0.480846	-0.319417	-1.321849
903	-0.543457	-0.484441	-0.319992	0.326054	-1.605482	-1.011512
904	4.534902	4.553709	-0.498963	-0.509429	0.401653	-1.573800
905	-0.162902	-0.159365	-0.205943	-1.033759	1.853397	1.257530
906	-0.357912	-0.378747	0.613089	0.929509	1.740566	-0.133690
907	-0.834890	-0.774945	0.553491	1.226758	0.170619	0.173700
908	1.853919	1.998891	1.361108	0.643827	-0.343395	-0.614717
909	0.559034	0.498583	-0.936838	0.700485	-0.018173	-0.781106
910	-0.808521	-0.703615	0.779738	-1.044559	0.361828	-2.090899
911	-0.716950	-0.688527	-1.809730	-1.037932	-0.676336	0.160729
912	1.039876	0.953581	-0.219800	0.406160	0.704469	-0.254209
913	-0.006559	0.028951	-0.504759	0.204814	-0.960616	-0.239064
914	-0.442120	-0.463660	-0.074947	-0.220194	-0.383694	1.738418
915	-0.465730	-0.499411	-0.873593	-0.353534	0.446968	-0.635559
916	-0.250269	-0.270598	0.108922	0.186943	-1.371520	-0.248336
917	-0.163480	-0.247813	-0.444731	0.518925	-1.354754	-0.635498
918	-0.701159	-0.708582	1.747623	0.568774	-0.053367	1.745879
919	0.211435	-0.048376	0.212770	1.310612	1.806197	0.513819
920	0.541364	0.087950	1.214127	1.336700	2.361296	0.216369
921	0.727348	0.533581	0.145181	0.401811	-0.411531	0.995818
922	-0.112823	-0.131954	-0.221095	-0.376894	0.964732	2.203621
923	-0.493806	-0.484812	-0.808556	1.044286	-1.717595	0.093573
924	0.077510	-0.033107	0.029139	0.498638	-0.385264	0.672533
925	-0.830729	-0.761913	0.622852	-1.155799	0.117107	1.180032
926	-0.649694	-0.645483	-0.779001	-0.399451	0.141012	0.308400
927	-0.679042	-0.513083	-0.343469	-1.520832	-1.324044	-0.973282
928	-0.766484	-0.712160	1.223978	0.005291	1.205971	1.753255
929	0.326135	0.389157	0.216150	0.902143	1.587099	-0.770980
930	-0.705391	-0.611802	-0.640352	-0.096009	-0.676256	-0.662049
931	1.907619	2.004142	0.830355	1.287913	0.991206	-1.623979
932	0.644932	0.509068	-0.006861	0.841028	-0.051434	0.768687
933	-0.892287	-0.806602	0.604220	-1.609557	-0.232170	0.664431
934	0.947054	0.774741	0.113761	0.711047	-0.571025	0.671875
935	-0.378605	-0.362831	-0.568937	-0.213891	-1.009617	-1.054852
936	-0.308849	-0.333699	0.705271	-0.218545	0.383797	0.418719
937	1.077341	0.993105	0.988545	0.818197	-0.043841	0.310871
938	-0.847861	-0.751674	1.405336	1.366696	0.557775	-0.303365
939	-0.251361	-0.218652	0.286814	0.935769	-0.626429	-0.592295
940	-0.866685	-0.791014	-0.660075	-0.926077	-0.451779	0.622928
941	0.006825	0.031590	0.135420	0.808775	-0.807778	-1.049667
942	2.307052	2.714570	-1.810784	-0.334114	-0.300526	-1.901682
943	-0.535727	-0.535938	-3.999367	-1.192027	0.470705	-0.376942
944	-0.865963	-0.815162	1.772881	0.133495	1.408815	0.665523
945	0.388127	0.291530	0.345638	0.362290	1.542232	0.826313
946	0.207673	0.097366	0.195844	-0.616554	0.760306	0.487900
947	-0.733911	-0.630207	1.498690	0.062958	-2.005173	-1.416878
948	0.063549	-0.073764	0.191754	-0.397916	-0.217057	1.692852
949	-0.897649	-0.826610	-0.157944	-2.229375	-0.718339	0.219551

	Importance
bachelors_percent	0.226505
female_percent	0.225420
VAP	0.167972
can_vote_percent	0.137320
candidatevotes	0.127624
voter_turnout	0.115160

	Importance
bachelors_percent	0.242982
female_percent	0.222382
VAP	0.178177
can_vote_percent	0.143347
candidatevotes	0.126458
voter_turnout	0.086654